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MESSADI, M., A. BESSAID, and A. TALEB-AHMED. "NEW CHARACTERIZATION METHODOLOGY FOR SKIN TUMORS CLASSIFICATION." Journal of Mechanics in Medicine and Biology 10, no. 03 (September 2010): 467–77. http://dx.doi.org/10.1142/s0219519410003514.
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Our objective in this paper is to introduce the efficacies of texture in the interpretation of color skin images. Melanoma is the most malignant skin tumor, growing in melanocytes, the cells responsible for pigmentation. This type of cancer is nowadays increasing rapidly; its related mortality rate increases by more modest and inversely proportional to the thickness of the tumor. This rate can be decreased by an earlier detection and better prevention. Using the features of skin tumors, such as color, symmetry, and border regularity, an attempt is made to determinate if the skin tumor is a melanoma or a benign tumor. In this work, we are interested by adding to form parameters such as the asymmetry (A) and the shape irregularities of skin tumors (B), the textural parameters to estimate colors in dermatoscopic images. In this case, the images are analyzed using textural parameters computed in several directions. These parameters and the form parameters are added to obtain a better classification results. A statistical analysis is performed over these ratios to select the most highly discriminating textural parameters. The method has been tested successfully on 144 images and we found significant differences between the lesions (melanoma and benign). Finally, these parameters (form and parameters of texture selected) are only use to classify the benign and malignancy of the skin lesion. A multilayer neural network is employed to differentiate between malignant tumors and benign lesions.
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Shawon, Mayinuzzaman, Kazi Fakhrul Abedin, Anik Majumder, Abir Mahmud, and Md Mahbub Chowdhury Mishu. "Identification of Risk of Occurring Skin Cancer (Melanoma) Using Convolutional Neural Network (CNN)." AIUB Journal of Science and Engineering (AJSE) 20, no. 2 (May 15, 2021): 47–51. http://dx.doi.org/10.53799/ajse.v20i2.140.
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Skin cancer is one of the most common malignancy in human, has drawn attention from researchers around the world. As skin cancer can turn into fatal if not treated in its earliest stages, the necessity of devising automated skin cancer diagnosis system that can automatically detect skin cancer efficiently in its earliest stage in a faster process than traditional one is of crucial importance. In this paper, a computer aided skin cancer diagnosis system based Convolutional Neural Network method has been shown. Our proposed system consists of five stages namely image acquisition, image preprocessing, image segmentation, feature extraction and classification We remove hair any noise from the images using dull then use median filter to smoothen the images. Next, k-means algorithm was applied for image segmentation on the preprocessed images. Finally, the segmented images were fed into CNN model for feature extraction and classification. The developed system can classify benign and melanoma type skin cancers from Dermoscopic images as accurate as 80.47%. While developing the skin cancer detection system, we compare accuracy score of other models such as Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) and Random Forest with our proposed system. The proposed method has been tested on ‘ISIC Challenge 2016’ test dataset and an accuracy rate of 80.47% was obtained for accurately classifying benign and malignant skin lesions by our proposed model.
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Ghazal, Taher M., Sajid Hussain, Muhammad Farhan Khan, Muhammad Adnan Khan, Raed A. T. Said, and Munir Ahmad. "Detection of Benign and Malignant Tumors in Skin Empowered with Transfer Learning." Computational Intelligence and Neuroscience 2022 (March 24, 2022): 1–9. http://dx.doi.org/10.1155/2022/4826892.
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Skin cancer is a major type of cancer with rapidly increasing victims all over the world. It is very much important to detect skin cancer in the early stages. Computer-developed diagnosis systems helped the physicians to diagnose disease, which allows appropriate treatment and increases the survival ratio of patients. In the proposed system, the classification problem of skin disease is tackled. An automated and reliable system for the classification of malignant and benign tumors is developed. In this system, a customized pretrained Deep Convolutional Neural Network (DCNN) is implemented. The pretrained AlexNet model is customized by replacing the last layers according to the proposed system problem. The softmax layer is modified according to binary classification detection. The proposed system model is well trained on malignant and benign tumors skin cancer dataset of 1920 images, where each class contains 960 images. After good training, the proposed system model is validated on 480 images, where the size of images of each class is 240. The proposed system model is analyzed using the following parameters: accuracy, sensitivity, specificity, Positive Predicted Values (PPV), Negative Predicted Value (NPV), False Positive Ratio (FPR), False Negative Ratio (FNR), Likelihood Ratio Positive (LRP), and Likelihood Ratio Negative (LRN). The accuracy achieved through the proposed system model is 87.1%, which is higher than traditional methods of classification.
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Korfiati, Aigli, Giorgos Livanos, Christos Konstandinou, Sophia Georgiou, and George Sakellaropoulos. "SKIN LESION CLASSIFICATION FROM DERMOSCOPY AND CLINICAL IMAGES WITH A DEEP LEARNING APPROACH." International Journal of Advanced Research 9, no. 10 (October 31, 2021): 1294–300. http://dx.doi.org/10.21474/ijar01/13681.
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Computer-aided diagnosis (CAD) systems based on deep learning approaches are now feasible due to the availability of big data and the availability of powerful computational resources.The medical image-based CAD systems are of great interest in numerous diseases, but especially for skin cancer diagnosis, deep learning models have been mostly developed for dermoscopy images. Models for clinical images are few, mainly due to the unavailability of big volumes of relevant data. However, CAD systems able to classify skin lesions from clinical images would be of great valueboth for the population and clinicians as an initial early screening of lesions that would leadpatients to visiting a dermatologist in case of suspicious lesions. This is even more pronounced in areas where there is lack of dermoscopy instruments. Thus, in this paper, we aimed to build a classifier based on bothdermoscopy and clinical images able to discriminate skin cancer from skin lesions. The classification is made among three benign and two malignant categories, which include Nevus, Benign but not nevus, Benign but suspicious for malignancy, Melanoma and Non-Melanocytic Carcinoma.The proposed deep learning classifier achieves an Area Under Curve ranging between 0.75 and 0.9 for the five examined categories.
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Hasan, Mohammed Rakeibul, Mohammed Ishraaf Fatemi, Mohammad Monirujjaman Khan, Manjit Kaur, and Atef Zaguia. "Comparative Analysis of Skin Cancer (Benign vs. Malignant) Detection Using Convolutional Neural Networks." Journal of Healthcare Engineering 2021 (December 11, 2021): 1–17. http://dx.doi.org/10.1155/2021/5895156.
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We live in a world where people are suffering from many diseases. Cancer is the most threatening of them all. Among all the variants of cancer, skin cancer is spreading rapidly. It happens because of the abnormal growth of skin cells. The increase in ultraviolet radiation on the Earth’s surface is also helping skin cancer spread in every corner of the world. Benign and malignant types are the most common skin cancers people suffer from. People go through expensive and time-consuming treatments to cure skin cancer but yet fail to lower the mortality rate. To reduce the mortality rate, early detection of skin cancer in its incipient phase is helpful. In today’s world, deep learning is being used to detect diseases. The convolutional neural network (CNN) helps to find skin cancer through image classification more accurately. This research contains information about many CNN models and a comparison of their working processes for finding the best results. Pretrained models like VGG16, Support Vector Machine (SVM), ResNet50, and self-built models (sequential) are used to analyze the process of CNN models. These models work differently as there are variations in their layer numbers. Depending on their layers and work processes, some models work better than others. An image dataset of benign and malignant data has been taken from Kaggle. In this dataset, there are 6594 images of benign and malignant skin cancer. Using different approaches, we have gained accurate results for VGG16 (93.18%), SVM (83.48%), ResNet50 (84.39%), Sequential_Model_1 (74.24%), Sequential_Model_2 (77.00%), and Sequential_Model_3 (84.09%). This research compares these outcomes based on the model’s work process. Our comparison includes model layer numbers, working process, and precision. The VGG16 model has given us the highest accuracy of 93.18%.
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Jinnai, Shunichi, Naoya Yamazaki, Yuichiro Hirano, Yohei Sugawara, Yuichiro Ohe, and Ryuji Hamamoto. "The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning." Biomolecules 10, no. 8 (July 29, 2020): 1123. http://dx.doi.org/10.3390/biom10081123.
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Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify images of melanoma, with accuracies comparable to those achieved by dermatologists. However, the performance of a CNN trained with only clinical images of a pigmented skin lesion in a clinical image classification task, in competition with dermatologists, has not been reported to date. In this study, we extracted 5846 clinical images of pigmented skin lesions from 3551 patients. Pigmented skin lesions included malignant tumors (malignant melanoma and basal cell carcinoma) and benign tumors (nevus, seborrhoeic keratosis, senile lentigo, and hematoma/hemangioma). We created the test dataset by randomly selecting 666 patients out of them and picking one image per patient, and created the training dataset by giving bounding-box annotations to the rest of the images (4732 images, 2885 patients). Subsequently, we trained a faster, region-based CNN (FRCNN) with the training dataset and checked the performance of the model on the test dataset. In addition, ten board-certified dermatologists (BCDs) and ten dermatologic trainees (TRNs) took the same tests, and we compared their diagnostic accuracy with FRCNN. For six-class classification, the accuracy of FRCNN was 86.2%, and that of the BCDs and TRNs was 79.5% (p = 0.0081) and 75.1% (p < 0.00001), respectively. For two-class classification (benign or malignant), the accuracy, sensitivity, and specificity were 91.5%, 83.3%, and 94.5% by FRCNN; 86.6%, 86.3%, and 86.6% by BCD; and 85.3%, 83.5%, and 85.9% by TRN, respectively. False positive rates and positive predictive values were 5.5% and 84.7% by FRCNN, 13.4% and 70.5% by BCD, and 14.1% and 68.5% by TRN, respectively. We compared the classification performance of FRCNN with 20 dermatologists. As a result, the classification accuracy of FRCNN was better than that of the dermatologists. In the future, we plan to implement this system in society and have it used by the general public, in order to improve the prognosis of skin cancer.
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Sella Veluswami, Jansi Rani, M. Ezhil Prasanth, K. Harini, and U. Ajaykumar. "Melanoma Skin Cancer Recognition and Classification Using Deep Hybrid Learning." Journal of Medical Imaging and Health Informatics 11, no. 12 (December 1, 2021): 3110–16. http://dx.doi.org/10.1166/jmihi.2021.3898.
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Melanoma skin cancer is a common disease that develops in the melanocytes that produces melanin. In this work, a deep hybrid learning model is engaged to distinguish the skin cancer and classify them. The dataset used contains two classes of skin cancer–benign and malignant. Since the dataset is imbalanced between the number of images in malignant lesions and benign lesions, augmentation technique is used to balance it. To improve the clarity of the images, the images are then enhanced using Contrast Limited Adaptive Histogram Equalization Technique (CLAHE) technique. To detect only the affected lesion area, the lesions are segmented using the neural network based ensemble model which is the result of combining the segmentation algorithms of Fully Convolutional Network (FCN), SegNet and U-Net which produces a binary image of the skin and the lesion, where the lesion is represented with white and the skin is represented by black. These binary images are further classified using different pre-trained models like Inception ResNet V2, Inception V3, Resnet 50, Densenet and CNN. Following that fine tuning of the best performing pre-trained model is carried out to improve the performance of classification. To further improve the performance of the classification model, a method of combining deep learning (DL) and machine learning (ML) is carried out. Using this hybrid approach, the feature extraction is done using DL models and the classification is performed by Support Vector Machine (SVM). This computer aided tool will assist doctors in diagnosing the disease faster than the traditional method. There is a significant improvement of nearly 4% increase in the performance of the proposed method is presented.
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Leon, Raquel, Beatriz Martinez-Vega, Himar Fabelo, Samuel Ortega, Veronica Melian, Irene Castaño, Gregorio Carretero, et al. "Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support." Journal of Clinical Medicine 9, no. 6 (June 1, 2020): 1662. http://dx.doi.org/10.3390/jcm9061662.
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Skin cancer is one of the most common forms of cancer worldwide and its early detection its key to achieve an effective treatment of the lesion. Commonly, skin cancer diagnosis is based on dermatologist expertise and pathological assessment of biopsies. Although there are diagnosis aid systems based on morphological processing algorithms using conventional imaging, currently, these systems have reached their limit and are not able to outperform dermatologists. In this sense, hyperspectral (HS) imaging (HSI) arises as a new non-invasive technology able to facilitate the detection and classification of pigmented skin lesions (PSLs), employing the spectral properties of the captured sample within and beyond the human eye capabilities. This paper presents a research carried out to develop a dermatological acquisition system based on HSI, employing 125 spectral bands captured between 450 and 950 nm. A database composed of 76 HS PSL images from 61 patients was obtained and labeled and classified into benign and malignant classes. A processing framework is proposed for the automatic identification and classification of the PSL based on a combination of unsupervised and supervised algorithms. Sensitivity and specificity results of 87.5% and 100%, respectively, were obtained in the discrimination of malignant and benign PSLs. This preliminary study demonstrates, as a proof-of-concept, the potential of HSI technology to assist dermatologists in the discrimination of benign and malignant PSLs during clinical routine practice using a real-time and non-invasive hand-held device.
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Uteng, Stig, Eduardo Quevedo, Gustavo M. Callico, Irene Castaño, Gregorio Carretero, Pablo Almeida, Aday Garcia, Javier A. Hernandez, and Fred Godtliebsen. "Curve-Based Classification Approach for Hyperspectral Dermatologic Data Processing." Sensors 21, no. 3 (January 20, 2021): 680. http://dx.doi.org/10.3390/s21030680.
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This paper shows new contributions in the detection of skin cancer, where we present the use of a customized hyperspectral system that captures images in the spectral range from 450 to 950 nm. By choosing a 7 × 7 sub-image of each channel in the hyperspectral image (HSI) and then taking the mean and standard deviation of these sub-images, we were able to make fits of the resulting curves. These fitted curves had certain characteristics, which then served as a basis of classification. The most distinct fit was for the melanoma pigmented skin lesions (PSLs), which is also the most aggressive malignant cancer. Furthermore, we were able to classify the other PSLs in malignant and benign classes. This gives us a rather complete classification method for PSLs with a novel perspective of the classification procedure by exploiting the variability of each channel in the HSI.
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Luqman Hakim, Zamah Sari, and Handhajani Handhajani. "Klasifikasi Citra Pigmen Kanker Kulit Menggunakan Convolutional Neural Network." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 2 (April 29, 2021): 379–85. http://dx.doi.org/10.29207/resti.v5i2.3001.
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Skin cancer is a very common form of cancer that can be found in the United States with annual treatment costs exceeding $ 8 billion. New innovations in the classification and detection of skin cancer using artificial neural networks continue to develop to help the medical and medical world in analyzing images accurately and accurately. Researchers propose to classify skin cancer pigments by focusing on two classes, namely non-melanocytic malignant and benign, where the skin cancer category which is classified into the non-melanocytic class is Actinic keratoses, Basal cell carcinoma. While skin cancers that are classified into Benign are Benign keratosis like lesions, dermatofibrama, vascular lessions. The method used in this study is Convolutional Neural Network (CNN) with a model architecture using 8 Convolutional 2D layers which have filters (16, 16, 32, 32, 64, 64, 128, 128). The first input layers are (20,20). and the following layers (5,5 and 3,3), the types of pooling used in this study are MaxPooling and AveragePooling. The Fully Connected Layer used is (256, 128) and uses a Dropout (0.2). The dataset is obtained from the International Skin Imaging Collaboration (ISIC) 2018 with a total of 10015 images. Based on the results of the test and evaluation reports, an accuracy of 75% is obtained. with the highest precision and recall values found in the Benign class, namely 0.80 and 0.82 respectively and the f1_score value of 0.81.
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Al-Khowarizmi, Al-Khowarizmi, and Suherman Suherman. "Classification of skin cancer images by applying simple evolving connectionist system." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (June 1, 2021): 421. http://dx.doi.org/10.11591/ijai.v10.i2.pp421-429.
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<span id="docs-internal-guid-eea5616b-7fff-5d26-eeb4-1d8c084ec93d"><span>Simple evolving connectionist system (SECoS) is one of data mining classification techniques that recognizing data based on the tested and the training data binding. Data recognition is achieved by aligning testing data to trained data pattern. SECoS uses a feedforward neural network but its hidden layer evolves so that each input layer does not perform epoch. SECoS distance has been modified with the normalized Euclidean distance formula to reduce error in training. This paper recognizes skin cancer by classifying benign malignant skin moles images using SECoS based on parameter combinations. The skin cancer classification has learning rate 1 of 0.3, learning rate 2 of 0.3, sensitivity threshold of 0.5, error threshold of 0.1 and MAPE is 0.5184845 with developing hidden node of 23. Skin cancer recognition by applying modified SECoS algorithm is proven more acceptable. Compared to other methods, SECoS is more robust to error variations.</span></span>
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Jaworek-Korjakowska, Joanna. "Computer-Aided Diagnosis of Micro-Malignant Melanoma Lesions Applying Support Vector Machines." BioMed Research International 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/4381972.
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Background. One of the fatal disorders causing death is malignant melanoma, the deadliest form of skin cancer. The aim of the modern dermatology is the early detection of skin cancer, which usually results in reducing the mortality rate and less extensive treatment. This paper presents a study on classification of melanoma in the early stage of development using SVMs as a useful technique for data classification.Method. In this paper an automatic algorithm for the classification of melanomas in their early stage, with a diameter under 5 mm, has been presented. The system contains the following steps: image enhancement, lesion segmentation, feature calculation and selection, and classification stage using SVMs.Results. The algorithm has been tested on 200 images including 70 melanomas and 130 benign lesions. The SVM classifier achieved sensitivity of 90% and specificity of 96%. The results indicate that the proposed approach captured most of the malignant cases and could provide reliable information for effective skin mole examination.Conclusions. Micro-melanomas due to the small size and low advancement of development create enormous difficulties during the diagnosis even for experts. The use of advanced equipment and sophisticated computer systems can help in the early diagnosis of skin lesions.
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Sen Saxena, Vivek, Prashant Johri, and Avneesh Kumar. "AI-Enabled Support System for Melanoma Detection and Classification." International Journal of Reliable and Quality E-Healthcare 10, no. 4 (October 2021): 58–75. http://dx.doi.org/10.4018/ijrqeh.2021100104.
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Skin lesion melanoma is the deadliest type of cancer. Artificial intelligence provides the power to classify skin lesions as melanoma and non-melanoma. The proposed system for melanoma detection and classification involves four steps: pre-processing, resizing all the images, removing noise and hair from dermoscopic images; image segmentation, identifying the lesion area; feature extraction, extracting features from segmented lesion and classification; and categorizing lesion as malignant (melanoma) and benign (non-melanoma). Modified GrabCut algorithm is employed to generate skin lesion. Segmented lesions are classified using machine learning algorithms such as SVM, k-NN, ANN, and logistic regression and evaluated on performance metrics like accuracy, sensitivity, and specificity. Results are compared with existing systems and achieved higher similarity index and accuracy.
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Felmingham, Claire, Samantha MacNamara, William Cranwell, Narelle Williams, Miki Wada, Nikki R. Adler, Zongyuan Ge, et al. "Improving Skin cancer Management with ARTificial Intelligence (SMARTI): protocol for a preintervention/postintervention trial of an artificial intelligence system used as a diagnostic aid for skin cancer management in a specialist dermatology setting." BMJ Open 12, no. 1 (January 2022): e050203. http://dx.doi.org/10.1136/bmjopen-2021-050203.
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IntroductionConvolutional neural networks (CNNs) can diagnose skin cancers with impressive accuracy in experimental settings, however, their performance in the real-world clinical setting, including comparison to teledermatology services, has not been validated in prospective clinical studies.Methods and analysisParticipants will be recruited from dermatology clinics at the Alfred Hospital and Skin Health Institute, Melbourne. Skin lesions will be imaged using a proprietary dermoscopic camera. The artificial intelligence (AI) algorithm, a CNN developed by MoleMap Ltd and Monash eResearch, classifies lesions as benign, malignant or uncertain. This is a preintervention/postintervention study. In the preintervention period, treating doctors are blinded to AI lesion assessment. In the postintervention period, treating doctors review the AI lesion assessment in real time, and have the opportunity to then change their diagnosis and management. Any skin lesions of concern and at least two benign lesions will be selected for imaging. Each participant’s lesions will be examined by a registrar, the treating consultant dermatologist and later by a teledermatologist. At the conclusion of the preintervention period, the safety of the AI algorithm will be evaluated in a primary analysis by measuring its sensitivity, specificity and agreement with histopathology where available, or the treating consultant dermatologists’ classification. At trial completion, AI classifications will be compared with those of the teledermatologist, registrar, treating dermatologist and histopathology. The impact of the AI algorithm on diagnostic and management decisions will be evaluated by: (1) comparing the initial management decision of the registrar with their AI-assisted decision and (2) comparing the benign to malignant ratio (for lesions biopsied) between the preintervention and postintervention periods.Ethics and disseminationHuman Research Ethics Committee (HREC) approval received from the Alfred Hospital Ethics Committee on 14 February 2019 (HREC/48865/Alfred-2018). Findings from this study will be disseminated through peer-reviewed publications, non-peer reviewed media and conferences.Trial registration numberNCT04040114.
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Margulis, Katherine, Albert S. Chiou, Sumaira Z. Aasi, Robert J. Tibshirani, Jean Y. Tang, and Richard N. Zare. "Distinguishing malignant from benign microscopic skin lesions using desorption electrospray ionization mass spectrometry imaging." Proceedings of the National Academy of Sciences 115, no. 25 (June 4, 2018): 6347–52. http://dx.doi.org/10.1073/pnas.1803733115.
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Detection of microscopic skin lesions presents a considerable challenge in diagnosing early-stage malignancies as well as in residual tumor interrogation after surgical intervention. In this study, we established the capability of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to distinguish between micrometer-sized tumor aggregates of basal cell carcinoma (BCC), a common skin cancer, and normal human skin. We analyzed 86 human specimens collected during Mohs micrographic surgery for BCC to cross-examine spatial distributions of numerous lipids and metabolites in BCC aggregates versus adjacent skin. Statistical analysis using the least absolute shrinkage and selection operation (Lasso) was employed to categorize each 200-µm-diameter picture element (pixel) of investigated skin tissue map as BCC or normal. Lasso identified 24 molecular ion signals, which are significant for pixel classification. These ion signals included lipids observed at m/z 200–1,200 and Krebs cycle metabolites observed at m/z < 200. Based on these features, Lasso yielded an overall 94.1% diagnostic accuracy pixel by pixel of the skin map compared with histopathological evaluation. We suggest that DESI-MSI/Lasso analysis can be employed as a complementary technique for delineation of microscopic skin tumors.
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Baig, Ramsha, Maryam Bibi, Anmol Hamid, Sumaira Kausar, and Shahzad Khalid. "Deep Learning Approaches Towards Skin Lesion Segmentation and Classification from Dermoscopic Images - A Review." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 5 (May 28, 2020): 513–33. http://dx.doi.org/10.2174/1573405615666190129120449.
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Background: Automated intelligent systems for unbiased diagnosis are primary requirement for the pigment lesion analysis. It has gained the attention of researchers in the last few decades. These systems involve multiple phases such as pre-processing, feature extraction, segmentation, classification and post processing. It is crucial to accurately localize and segment the skin lesion. It is observed that recent enhancements in machine learning algorithms and dermoscopic techniques reduced the misclassification rate therefore, the focus towards computer aided systems increased exponentially in recent years. Computer aided diagnostic systems are reliable source for dermatologists to analyze the type of cancer, but it is widely acknowledged that even higher accuracy is needed for computer aided diagnostic systems to be adopted practically in the diagnostic process of life threatening diseases. Introduction: Skin cancer is one of the most threatening cancers. It occurs by the abnormal multiplication of cells. The core three types of skin cells are: Squamous, Basal and Melanocytes. There are two wide classes of skin cancer; Melanocytic and non-Melanocytic. It is difficult to differentiate between benign and malignant melanoma, therefore dermatologists sometimes misclassify the benign and malignant melanoma. Melanoma is estimated as 19th most frequent cancer, it is riskier than the Basel and Squamous carcinoma because it rapidly spreads throughout the body. Hence, to lower the death risk, it is critical to diagnose the correct type of cancer in early rudimentary phases. It can occur on any part of body, but it has higher probability to occur on chest, back and legs. Methods: The paper presents a review of segmentation and classification techniques for skin lesion detection. Dermoscopy and its features are discussed briefly. After that Image pre-processing techniques are described. A thorough review of segmentation and classification phases of skin lesion detection using deep learning techniques is presented Literature is discussed and a comparative analysis of discussed methods is presented. Conclusion: In this paper, we have presented the survey of more than 100 papers and comparative analysis of state of the art techniques, model and methodologies. Malignant melanoma is one of the most threating and deadliest cancers. Since the last few decades, researchers are putting extra attention and effort in accurate diagnosis of melanoma. The main challenges of dermoscopic skin lesion images are: low contrasts, multiple lesions, irregular and fuzzy borders, blood vessels, regression, hairs, bubbles, variegated coloring and other kinds of distortions. The lack of large training dataset makes these problems even more challenging. Due to recent advancement in the paradigm of deep learning, and specially the outstanding performance in medical imaging, it has become important to review the deep learning algorithms performance in skin lesion segmentation. Here, we have discussed the results of different techniques on the basis of different evaluation parameters such as Jaccard coefficient, sensitivity, specificity and accuracy. And the paper listed down the major achievements in this domain with the detailed discussion of the techniques. In future, it is expected to improve results by utilizing the capabilities of deep learning frameworks with other pre and post processing techniques so reliable and accurate diagnostic systems can be built.
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Shrivastava, Vaidik, Ashwini Tangde, Anil Joshi, and Rajan Bindu. "Clinicopathological study of skin tumours." International Journal of Research in Medical Sciences 7, no. 5 (April 26, 2019): 1712. http://dx.doi.org/10.18203/2320-6012.ijrms20191664.
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Background: Skin cancers are relatively uncommon malignancies worldwide, but the incidence of skin cancers has progressively increased over the last few decades. The distinction between benign and malignant neoplasm are more difficult to define when they appear in skin than when found elsewhere and histopathological examination is frequently required to establish a definitive diagnosis. Diagnosis of any skin tumours can be done by correlating clinical features and histological features. The aim and objective were to study age-sex wise distribution, clinical presentation and histopathological spectrum of various skin tumours.Methods: This is a retrospective study of three years conducted in the Department of Pathology, Government Medical College, Aurangabad, India from December 2015 to December 2018. Specimens received from Department of Dermatology were fixed in formalin and after adequately processing the sections were stained routinely with H and E stain and properly evaluated for histopathological examination. This study includes tumors of epidermis along with melanogenic tumors and skin appendageal tumors. The data collected was tabulated, analysed and compared to other similar studies.Results: The study consists of 130 cases. The ratio of male to female was 1.24:1. Head and neck region (48.46%) was the most common site observed where skin lesions were present followed by extremities (37.69%). Most of the malignant tumours were presented with non-healing ulcers (30.76%) and Noduloulcerative lesions (20.33%). Out of 130 cases, 83 (63.84%) were benign whereas 47 (36.15%) were malignant tumour. According to WHO classification, keratinocytic tumour 55 (42.30%) was the most common tumour type in the present study. Skin adnexal tumours and melanocytic tumours were observed in 54 (41.53%) and 21 (16.15%) respectively.Conclusions: The skin is a complex organ. Because of complexity of skin, a wide range of diseases can develop from the skin. The majority of benign neoplasms are from skin adnexal group whereas most common malignant neoplasm were from keratinocytic group. Skin adnexal tumors can occur anywhere in the body, however head and neck region constitute the most common site. Skin adnexal tumours are clinically often misdiagnosed, so histopathological examination remains gold standard for their correct diagnosis and for their differentiation between benign and malignant neoplasm.
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Torti, Emanuele, Raquel Leon, Marco La Salvia, Giordana Florimbi, Beatriz Martinez-Vega, Himar Fabelo, Samuel Ortega, Gustavo M. Callicó, and Francesco Leporati. "Parallel Classification Pipelines for Skin Cancer Detection Exploiting Hyperspectral Imaging on Hybrid Systems." Electronics 9, no. 9 (September 13, 2020): 1503. http://dx.doi.org/10.3390/electronics9091503.
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The early detection of skin cancer is of crucial importance to plan an effective therapy to treat the lesion. In routine medical practice, the diagnosis is based on the visual inspection of the lesion and it relies on the dermatologists’ expertise. After a first examination, the dermatologist may require a biopsy to confirm if the lesion is malignant or not. This methodology suffers from false positives and negatives issues, leading to unnecessary surgical procedures. Hyperspectral imaging is gaining relevance in this medical field since it is a non-invasive and non-ionizing technique, capable of providing higher accuracy than traditional imaging methods. Therefore, the development of an automatic classification system based on hyperspectral images could improve the medical practice to distinguish pigmented skin lesions from malignant, benign, and atypical lesions. Additionally, the system can assist general practitioners in first aid care to prevent noncritical lesions from reaching dermatologists, thereby alleviating the workload of medical specialists. In this paper is presented a parallel pipeline for skin cancer detection that exploits hyperspectral imaging. The computational times of the serial processing have been reduced by adopting multicore and many-core technologies, such as OpenMP and CUDA paradigms. Different parallel approaches have been combined, leading to the development of fifteen classification pipeline versions. Experimental results using in-vivo hyperspectral images show that a hybrid parallel approach is capable of classifying an image of 50 × 50 pixels with 125 bands in less than 1 s.
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Thapar, Puneet, Manik Rakhra, Gerardo Cazzato, and Md Shamim Hossain. "A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification." Journal of Healthcare Engineering 2022 (April 18, 2022): 1–21. http://dx.doi.org/10.1155/2022/1709842.
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Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning based on convolution neural network (CNN) and annotated skin photos exhibit improved results. In this respect, the paper presents a reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals’ visual perception and diagnostic abilities to discriminate benign from malignant lesions. The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction of the RoI marked as the best segmentation result obtained using the Grasshopper Optimization Algorithm (GOA). The skin lesions are classified into two groups using CNN against three data sets, namely, ISIC-2017, ISIC-2018, and PH-2 data sets. The proposed segmentation and classification techniques’ results are assessed in terms of classification accuracy, sensitivity, specificity, F-measure, precision, MCC, dice coefficient, and Jaccard index, with an average classification accuracy of 98.42 percent, precision of 97.73 percent, and MCC of 0.9704 percent. In every performance measure, our suggested strategy exceeds previous work.
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Zghal, Nadia Smaoui, and Nabil Derbel. "Melanoma Skin Cancer Detection based on Image Processing." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 1 (January 6, 2020): 50–58. http://dx.doi.org/10.2174/1573405614666180911120546.
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Background: Skin cancer is one of the most common forms of cancers among humans. It can be classified as non-melanoma and melanoma. Although melanomas are less common than non-melanomas, the former is the most common cause of mortality. Therefore, it becomes necessary to develop a Computer-aided Diagnosis (CAD) aiming to detect this kind of lesion and enable the diagnosis of the disease at an early stage in order to augment the patient’s survival likelihood. Aims: This paper aims to develop a simple method capable of detecting and classifying skin lesions using dermoscopy images based on ABCD rules. Methods: The proposed approach follows four steps. 1) The preprocessing stage consists of filtering and contrast enhancing algorithms. 2) The segmentation stage aims at detecting the lesion. 3) The feature extraction stage based on the calculation of the four parameters which are asymmetry, border irregularity, color and diameter. 4) The classification stage based on the summation of the four extracted parameters multiplied by their weights yields the total dermoscopy value (TDV); hence, the lesion is classified into benign, suspicious or malignant. The proposed approach is implemented in the MATLAB environment and the experiment is based on PH2 database containing suspicious melanoma skin cancer. Results and Conclusion: Based on the experiment, the accuracy of the developed approach is 90%, which reflects its reliability.
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RAJU K, RAINA, and S. Swapna Kumar. "A Comparative Study of Various Techniques used for Melanoma Detection." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 11 (November 30, 2017): 44. http://dx.doi.org/10.23956/ijarcsse.v7i11.466.
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Skin cancer is one of the most fatal disease. It is easily curable, when it is detected in its beginning stage. Early detection of melanoma through accurate techniques and innovative technologies has the greatest potential for decreasing mortality associated with this disease. Mainly there are four steps for detecting melanoma which includes preprocessing, segmentation, feature extraction and classification. The preprocessing stage will remove all the artifacts associated with the lesion. The exact boundaries of lesion are identified from normal skin through segmentation method. Feature extraction stage is used for calculating and obtaining different parameters of the lesion region. The final stage is to classify the lesion as benign or malignant. In this paper different types of segmentation methods and classification methods are described. Both of these stages are accurately implemented to reach the final detection of the lesion.
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K, Raina Raju, and S. Swapna Kumar. "A Comparative Study of Various Techniques used for Melanoma Detection." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 9 (October 31, 2017): 52. http://dx.doi.org/10.23956/ijarcsse.v7i9.411.
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Skin cancer is one of the most fatal disease. It is easily curable, when it is detected in its beginning stage. Early detection of melanoma through accurate techniques and innovative technologies has the greatest potential for decreasing mortality associated with this disease. Mainly there are four steps for detecting melanoma which includes preprocessing, segmentation, feature extraction and classification. The preprocessing stage will remove all the artifacts associated with the lesion. The exact boundaries of lesion are identified from normal skin through segmentation method. Feature extraction stage is used for calculating and obtaining different parameters of the lesion region. The final stage is to classify the lesion as benign or malignant. In this paper different types of segmentation methods and classification methods are described. Both of these stages are accurately implemented to reach the final detection of the lesion.
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Kostopoulos, Spiros, Dimitris Glotsos, Pantelis Asvestas, Christos Konstandinou, George Xenogiannopoulos, Konstantinos Sidiropoulos, Eirini-Konstantina Nikolatou, et al. "AN ENSEMBLE TEMPLATE MATCHING AND CONTENT-BASED IMAGE RETRIEVAL SCHEME TOWARDS EARLY STAGE DETECTION OF MELANOMA." Image Analysis & Stereology 35, no. 3 (December 8, 2016): 137. http://dx.doi.org/10.5566/ias.1446.
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Malignant melanoma represents the most dangerous type of skin cancer. In this study we present an ensemble classification scheme, employing the mutual information, the cross-correlation and the clustering based on proximity of image features methods, for early stage assessment of melanomas on plain photography images. The proposed scheme performs two main operations. First, it retrieves the most similar, to the unknown case, image samples from an available image database with verified benign moles and malignant melanoma cases. Second, it provides an automated estimation regarding the nature of the unknown image sample based on the majority of the most similar images retrieved from the available database. Clinical material comprised 75 melanoma and 75 benign plain photography images collected from publicly available dermatological atlases. Results showed that the ensemble scheme outperformed all other methods tested in terms of accuracy with 94.9±1.5%, following an external cross-validation evaluation methodology. The proposed scheme may benefit patients by providing a second opinion consultation during the self-skin examination process and the physician by providing a second opinion estimation regarding the nature of suspicious moles that may assist towards decision making especially for ambiguous cases, safeguarding, in this way from potential diagnostic misinterpretations.
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Kaur, Ranpreet, Hamid GholamHosseini, Roopak Sinha, and Maria Lindén. "Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images." Sensors 22, no. 3 (February 2, 2022): 1134. http://dx.doi.org/10.3390/s22031134.
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Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using a deep learning approach as a machine vision tool can overcome some challenges. This research proposes an automated melanoma classifier based on a deep convolutional neural network (DCNN) to accurately classify malignant vs. benign melanoma. The structure of the DCNN is carefully designed by organizing many layers that are responsible for extracting low to high-level features of the skin images in a unique fashion. Other vital criteria in the design of DCNN are the selection of multiple filters and their sizes, employing proper deep learning layers, choosing the depth of the network, and optimizing hyperparameters. The primary objective is to propose a lightweight and less complex DCNN than other state-of-the-art methods to classify melanoma skin cancer with high efficiency. For this study, dermoscopic images containing different cancer samples were obtained from the International Skin Imaging Collaboration datastores (ISIC 2016, ISIC2017, and ISIC 2020). We evaluated the model based on accuracy, precision, recall, specificity, and F1-score. The proposed DCNN classifier achieved accuracies of 81.41%, 88.23%, and 90.42% on the ISIC 2016, 2017, and 2020 datasets, respectively, demonstrating high performance compared with the other state-of-the-art networks. Therefore, this proposed approach could provide a less complex and advanced framework for automating the melanoma diagnostic process and expediting the identification process to save a life.
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Ahmed, Bakhan Tofiq. "Data mining techniques for lung and breast cancer diagnosis: A review." International Journal of Informatics and Communication Technology (IJ-ICT) 10, no. 2 (August 1, 2021): 93. http://dx.doi.org/10.11591/ijict.v10i2.pp93-103.
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<p>Today, cancer counted as the riskier disease than the other diseases in the globe. There are many cancer forms like leukemia, skin cancer, and stomach cancer but lung and breast cancer are the most common forms that many people suffered from. Cancer is the disease that cell has grown rapidly and abnormally that is why treating it is somehow tough in some cases but it can be controlled if it is detected in the initial stage. Data-mining classification algorithms had a vital role in predicting and recognizing both benign and malignant cell. Several classifiers are available to classify the usual and unusual cells such as decision-tree, artificial-neural net, SVM, and KNN. This paper presents a systematic review about the most well-known data-mining classification algorithms for lung and breast cancer diagnose. A brief review about KDD and the data-mining concept has demonstrated. The Decision-Tree (D-Tree), ANN, Support-vector-machine, and naïve Bayes classifier that is widely utilized in the biomedical field has been reviewed along with the some algorithms such as C4.5, Cart, and Iterative -Dichotomiser 3 ‘ID3’. A comparison has been done among various reviewed papers in terms of accuracy that used various data-mining classification algorithms to propose the lung and breast cancer diagnosis system. The experimental results of the reviewed papers showed that the Multilayer Perceptron (MLP) and Logistic Regression (LR) gave a higher accuracy of 99.04% and 98.1%, respectively.</p>
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Jones, Leah, Michael Jameson, and Amanda Oakley. "Remote Skin Cancer Diagnosis: Adding Images to Electronic Referrals Is More Efficient Than Wait-Listing for a Nurse-Led Imaging Clinic." Cancers 13, no. 22 (November 20, 2021): 5828. http://dx.doi.org/10.3390/cancers13225828.
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We undertook a retrospective comparison of two teledermatology pathways that provide diagnostic and management advice for suspected skin cancers, to evaluate the time from referral to diagnosis and its concordance with histology. Primary Care doctors could refer patients to either the Virtual Lesion Clinic (VLC), a nurse-led community teledermoscopy clinic or, more recently, to the Suspected Skin Cancer (SSC) pathway, which requires them to attach regional, close-up, and dermoscopic images. The primary objective of this study was to determine the comparative time course between the SSC pathway and VLC. Secondary objectives included comparative diagnostic concordance, skin lesion classification, and evaluation of missed skin lesions during subsequent follow-up. VLC referrals from July to December 2016 and 2020 were compared to SSC referrals from July to December 2020. 408 patients with 682 lesions in the VLC cohort were compared with 480 patients with 548 lesions from the 2020 SSC cohort, matched for age, sex, and ethnicity, including histology where available. Median time (SD) from referral to receipt of teledermatology advice was four (2.8) days and 50 (43.0) days for the SSC and VLC cohorts, respectively (p < 0.001). Diagnostic concordance between teledermatologist and histopathologist for benign versus malignant lesions was 70% for 114 lesions in the SSC cohort, comparable to the VLC cohort (71% of 122 lesions). Referrals from primary care, where skin lesions were imaged with variable devices and quality resulted in faster specialist advice with similar diagnostic performance compared to high-quality imaging at nurse-led specialist dermoscopy clinics.
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Pelicano, Ana Catarina, Maria C. T. Gonçalves, Daniela M. Godinho, Tiago Castela, M. Lurdes Orvalho, Nuno A. M. Araújo, Emily Porter, and Raquel C. Conceição. "Development of 3D MRI-Based Anatomically Realistic Models of Breast Tissues and Tumours for Microwave Imaging Diagnosis." Sensors 21, no. 24 (December 10, 2021): 8265. http://dx.doi.org/10.3390/s21248265.
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Breast cancer diagnosis using radar-based medical MicroWave Imaging (MWI) has been studied in recent years. Realistic numerical and physical models of the breast are needed for simulation and experimental testing of MWI prototypes. We aim to provide the scientific community with an online repository of multiple accurate realistic breast tissue models derived from Magnetic Resonance Imaging (MRI), including benign and malignant tumours. Such models are suitable for 3D printing, leveraging experimental MWI testing. We propose a pre-processing pipeline, which includes image registration, bias field correction, data normalisation, background subtraction, and median filtering. We segmented the fat tissue with the region growing algorithm in fat-weighted Dixon images. Skin, fibroglandular tissue, and the chest wall boundary were segmented from water-weighted Dixon images. Then, we applied a 3D region growing and Hoshen-Kopelman algorithms for tumour segmentation. The developed semi-automatic segmentation procedure is suitable to segment tissues with a varying level of heterogeneity regarding voxel intensity. Two accurate breast models with benign and malignant tumours, with dielectric properties at 3, 6, and 9 GHz frequencies have been made available to the research community. These are suitable for microwave diagnosis, i.e., imaging and classification, and can be easily adapted to other imaging modalities.
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Balasundaram, Shanmugham, Revathi Balasundaram, Ganesan Rasuthevar, Christeena Joseph, Annie Grace Vimala, Nanmaran Rajendiran, and Baskaran Kaliyamurthy. "Automated Detection and Classification of Breast Cancer Nuclei with Deep Convolutional Neural Network." Journal of ICT Research and Applications 15, no. 2 (October 7, 2021): 139–51. http://dx.doi.org/10.5614/itbj.ict.res.appl.2021.15.2.3.
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Heterogeneous regions present in tissue with respect to cancer cells are of various types. This study aimed to analyze and classify the morphological features of the nucleus and cytoplasm regions of tumor cells. This tissue morphology study was established through invasive ductal breast cancer histopathology images accessed from the Databiox public dataset. Automatic detection and classification was carried out by means of the computer analytical tool of deep learning algorithm. Residual blocks with short skip were employed with hidden layers of preserved spatial information. A ResNet-based convolutional neural network was adapted to perform end-to-end segmentation of breast cancer nuclei. Nuclei regions were identified through color and tubular structure morphological features. Based on the segmented and extracted images, classification of benign and malignant breast cancer cells was done to identify tumors. The results indicated that the proposed method could successfully segment and classify breast tumors with an average Dice score of 90.68%, sensitivity = 98.64, specificity = 98.68, and accuracy = 98.82.
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Kalwa, Upender, Christopher Legner, Taejoon Kong, and Santosh Pandey. "Skin Cancer Diagnostics with an All-Inclusive Smartphone Application." Symmetry 11, no. 6 (June 13, 2019): 790. http://dx.doi.org/10.3390/sym11060790.
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Among the different types of skin cancer, melanoma is considered to be the deadliest and is difficult to treat at advanced stages. Detection of melanoma at earlier stages can lead to reduced mortality rates. Desktop-based computer-aided systems have been developed to assist dermatologists with early diagnosis. However, there is significant interest in developing portable, at-home melanoma diagnostic systems which can assess the risk of cancerous skin lesions. Here, we present a smartphone application that combines image capture capabilities with preprocessing and segmentation to extract the Asymmetry, Border irregularity, Color variegation, and Diameter (ABCD) features of a skin lesion. Using the feature sets, classification of malignancy is achieved through support vector machine classifiers. By using adaptive algorithms in the individual data-processing stages, our approach is made computationally light, user friendly, and reliable in discriminating melanoma cases from benign ones. Images of skin lesions are either captured with the smartphone camera or imported from public datasets. The entire process from image capture to classification runs on an Android smartphone equipped with a detachable 10x lens, and processes an image in less than a second. The overall performance metrics are evaluated on a public database of 200 images with Synthetic Minority Over-sampling Technique (SMOTE) (80% sensitivity, 90% specificity, 88% accuracy, and 0.85 area under curve (AUC)) and without SMOTE (55% sensitivity, 95% specificity, 90% accuracy, and 0.75 AUC). The evaluated performance metrics and computation times are comparable or better than previous methods. This all-inclusive smartphone application is designed to be easy-to-download and easy-to-navigate for the end user, which is imperative for the eventual democratization of such medical diagnostic systems.
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Jasti, V. Durga Prasad, Abu Sarwar Zamani, K. Arumugam, Mohd Naved, Harikumar Pallathadka, F. Sammy, Abhishek Raghuvanshi, and Karthikeyan Kaliyaperumal. "Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis." Security and Communication Networks 2022 (March 9, 2022): 1–7. http://dx.doi.org/10.1155/2022/1918379.
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Breast cancer is the most lethal type of cancer for all women worldwide. At the moment, there are no effective techniques for preventing or curing breast cancer, as the source of the disease is unclear. Early diagnosis is a highly successful means of detecting and managing breast cancer, and early identification may result in a greater likelihood of complete recovery. Mammography is the most effective method of detecting breast cancer early. Additionally, this instrument enables the detection of additional illnesses and may provide information about the nature of cancer, such as benign, malignant, or normal. This article discusses an evolutionary approach for classifying and detecting breast cancer that is based on machine learning and image processing. This model combines image preprocessing, feature extraction, feature selection, and machine learning techniques to aid in the classification and identification of skin diseases. To enhance the image’s quality, a geometric mean filter is used. AlexNet is used for extracting features. Feature selection is performed using the relief algorithm. For disease categorization and detection, the model makes use of the machine learning techniques such as least square support vector machine, KNN, random forest, and Naïve Bayes. The experimental investigation makes use of MIAS data collection. This proposed technology is advantageous for accurately identifying breast cancer disease using image analysis.
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Sánchez-Reyes, Luz-María, Juvenal Rodríguez-Reséndiz, Sebastián Salazar-Colores, Gloria Nélida Avecilla-Ramírez, and Gerardo Israel Pérez-Soto. "A High-Accuracy Mathematical Morphology and Multilayer Perceptron-Based Approach for Melanoma Detection." Applied Sciences 10, no. 3 (February 6, 2020): 1098. http://dx.doi.org/10.3390/app10031098.
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According to the World Health Organization (WHO), melanoma is the most severe type of skin cancer and is the leading cause of death from skin cancer worldwide. Certain features of melanoma include size, shape, color, or texture changes of a mole. In this work, a novel, robust and efficient method for the detection and classification of melanoma in simple and dermatological images is proposed. It is achieved by using HSV (Hue, Saturation, Value) color space along with mathematical morphology and a Gaussian filter to detect the region of interest and estimate four descriptors: symmetry, edge, color, and size. Although these descriptors have been used for several years, the way they are computed for this proposal is one of the things that enhances the results. Subsequently, a multilayer perceptron is employed to classify between malignant and benign melanoma. Three datasets of simple and dermatological images commonly used in the literature were employed to train and evaluate the performance of the proposed method. According to k-fold cross-validation, the method outperforms three state-of-art works, achieving an accuracy of 98.5% and 98.6%, a sensitivity of 96.68% and 98.05%, and a specificity of 98.15%, and 98.01%, in simple and dermatological images, respectively. The results have proven that its use as an assistive device for the detection of melanoma would improve reliability levels compared to conventional methods.
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Yas, Qahtan M., A. A. Zaidan, B. B. Zaidan, M. Hashim, and C. K. Lim. "A Systematic Review on Smartphone Skin Cancer Apps: Coherent Taxonomy, Motivations, Open Challenges and Recommendations, and New Research Direction." Journal of Circuits, Systems and Computers 27, no. 05 (February 6, 2018): 1830003. http://dx.doi.org/10.1142/s0218126618300039.
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Objective: This research aims to survey the efforts of researchers in response to the new and disruptive technology of skin cancer apps, map the research landscape from the literature onto coherent taxonomy, and determine the basic characteristics of this emerging field. In addition, this research looks at the motivation behind using Smartphone apps in the diagnosis of skin cancer and in health care and the open challenges that impede the utility of this technology. This study offers valuable recommendations to improve the acceptance and use of medical apps in the literature. Methods: We conducted a comprehensive survey using the keywords “skin cancer,” “apps,” and “Smartphone” or “m-Health” in different variations to find all the relevant articles in three major databases: Web of Science, Science Direct, and IEEE Xplore. These databases broadly cover medical and technical literature. Results: We found 110 articles after a comprehensive survey of the literature. Out of the 110 articles, 46 present actual attempts to develop and design medical apps or share certain experiences of doing so. Twenty-eight articles consist of analytical studies on the incidence of skin cancer, the classification of malignant cancer or benign cancer, and the methods of prevention and diagnosis. Twenty-two articles comprise studies that range from the evaluative or comparative study of apps to the exploration of the desired features for skin cancer detection. Fourteen articles consist of reviews and surveys that refer to actual apps or the literature to describe medical apps for a specific specialty, disease, or skin cancer and provide a general overview of the technology. New research direction: With the exception of the 110 papers reviewed earlier in results section, the new directions of this research were described. In state-of-the-art, no particular study presenting watermarking and stenography approaches for any type of skin cancer images based on Smartphone apps is available. Discussion: Researchers have attempted to develop and improve skin cancer apps in several ways since 2011. However, several areas or aspects require further attention. All the articles, regardless of their research focus, attempt to address the challenges that impede the full utility of skin cancer apps and offer recommendations to mitigate their drawbacks. Conclusions: Research on skin cancer apps is active and efficient. This study contributes to this area of research by providing a detailed review of the available options and problems to allow other researchers and participants to further develop skin cancer apps, and the new directions of this research were described.
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Everett, S. A., V. M. McErlane, K. F. McLeod, F. M. Daley, P. R. Barber, B. Vojnovic, P. D. Nathan, et al. "Profiling cytochrome P450 CYP1 enzyme expression in primary melanoma and disseminated disease utilizing spectral imaging microscopy (SIM)." Journal of Clinical Oncology 25, no. 18_suppl (June 20, 2007): 8556. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.8556.
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8556 Background: The aim of this study was to profile cytochrome CYP1 family (CYP1A1/1A2, and CYP1B1) mono-oxygenase enzymes during the malignant progression of primary melanoma and metastatic disease. Methods: Tissue microarrays of primary (n = 75), and metastatic (n = 104) melanoma were constructed with the patient demographics: (1) primary melanoma; age 22 to 93 (median 59); sex M/F 36/44; Breslow thickness 0.4 to 15 mm (median 2.5 mm); ulceration 25/80, and (2) metastatic melanoma; age 26 to 92 (median 60 mm); sex M/F 54/49; ulceration 30/104; number of nodes 1 to 15 (median 2); extra-capsular spread 20/95. CYP1 protein was detected by IHC using validated selective poly- and monoclonal antibodies. Vector SG (grey) stain for CYP1 was used with nuclear fast red counterstain to aid spectral resolution from background melanin. Staining intensity was scored visually (negative 0, weak 1, moderate 2, strong 3) and using SIM at every pixel of a captured image of each melanoma core. Reference spectra of individual chromophores were used to spectrally ‘un-mix’ CYP1 staining before the mean normalised absorbance intensity was determined. Grading was by the 2002 AJCC classification system: primary stage I n = 27 (1A 8, 1B 19), and stage II n = 48 (2A 22, 2B 16, 2C 10), lymph node metastasis stage III n = 98 (3B 53, 3C 45), visceral metastasis stage IV n = 6. Normal skin (n = 27), benign naevi (n = 14), and dysplastic naevi (n = 21) were also included. Results: CYP1B1 was not in normal skin but was over-expressed in both primary and metastatic melanoma (visual: 71% & 65%, SIM: 91% & 83%). Primary melanoma (stage I & II) was significantly greater (p = 0.004) than metastasis (stage III & IV). CYP1B1 did not correlate with ulceration or Breslow thickness but did correlate with N stage lymph node metastasis (p = 0.005). CYP1B1 expression in dysplastic naevi indicated up-regulation at an early stage of melanoma progression. CYP1A1/1A2 was not expressed in normal skin nor primary/metastatic melanoma. Conclusions: CYP1B1 protein expression is maintained with advancing AJCC stage from primary through to visceral metastasis. Future work will seek to correlate protein expression with functionality with a view to exploiting CYP1B1 in the enzyme/prodrug therapy of malignant melanoma. No significant financial relationships to disclose.
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Matasar, Matthew J., Weiji Shi, Jonathan Silberstien, Julie T. Feldstein, Daniel Filippa, Andrew D. Zelenetz, and Ariela Noy. "Expert Second Opinion Pathology Review of Lymphoma in the Era of the World Health Organization Classification." Blood 110, no. 11 (November 16, 2007): 3317. http://dx.doi.org/10.1182/blood.v110.11.3317.3317.
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Abstract Background: The effective management of lymphoma depends upon an accurate and precise pathologic diagnosis. However, the classification of lymphoma continues to evolve. Reports addressing the role of second opinion expert pathology review have found varying impact, and little is known regarding the predictors of a change in diagnosis. Furthermore, the impact of the World Health Organization (WHO) classification of lymphomas over the 5 years following their formal publication has not been formally assessed. Methods: All outside pathology is reviewed at Memorial Sloan-Kettering Cancer Center (MSKCC) before a clinical opinion is finalized. We performed a chart review of all externally referred lymphoma cases from 1/1/01 to 6/30/01 and from 1/1/06 to 6/30/06 with second opinions from MSKCC hematopathology. Statistical analysis was performed using Chi-square or Fisher’s exact test for univariate analysis and logistic regression for multivariate analysis. Results: 719 patients (365 in 2001, 354 in 2006) met inclusion criteria. Diagnostic revisions were classified as major or minor; major changes were those that would lead to management changes as per National Comprehensive Cancer Network guidelines. 122 patients (18% in 2001, 16% in 2006) had a major diagnostic revision and an additional 22 (4% in 2001, 2% in 2006) had confirmation of major revisions rendered previously at second opinion from another National Cancer Institute Comprehensive Cancer Center (CCC). This did not change significantly by era, with 79 major revisions (22%) in 2001 and 65 (18%) in 2006 (P=NS). An additional 55 patients [24 (7%) in 2001, 31 (9%) in 2006] received minor revisions. Common categories of major revision included changing from nondiagnostic/ambiguous to definitive [6 in 2001, 8 in 2006], definitive to nondiagnostic [9 in 2001, 9 in 2006], malignant to benign [1 in 2001, 6 in 2006], indolent B-cell lymphoma (BCL) to aggressive BCL [15 in 2001, 8 in 2006], and aggressive BCL to indolent BCL [4 in 2001, 1 in 2006]. Major diagnostic revision was significantly associated with additional immunohistochemistry (IHC) testing in 2001 (OR=2.3; 95%CI 1.3, 4). In 2006, additional IHC (OR=1.8; 95%CI 1, 3.4), repeat biopsy (OR=3.1; 95%CI 1.2, 8.0), and skin biopsy (versus lymph node biopsy; OR 3.3; 95%CI 1.6, 7.0) were significantly associated with major revision. Two of the 7 patients reclassified as benign received revisions based on additional IHC, whereas 7 of the 14 patients reclassified as malignant were revised due to either additional IHC (4) or repeat biopsy (3). No effect was seen by biopsy type, nor were patient gender, age, race or ethnicity associated with odds of major revision. Of cases seen first at another CCC, 12% in 2001 and 16% in 2006 received major revisions, compared to 19% (2001) and 16% (2006) of other cases; these differences were not statistically significant. Conclusion: The rate of clinically meaningful diagnostic revisions at second opinion expert pathology review was high for patients seen at MSKCC, and remained so despite five years of increased familiarity with the WHO classification schema. These data confirm the fact that an appropriate evaluation, including detailed IHC and an adequate biopsy specimen, plays a central role in the accurate diagnosis of lymphoma. The high rates of diagnostic revision reported here lend support to the routine application of expert second opinion hematopathology review.
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Akilandeswari, A., D. Sungeetha, Christeena Joseph, K. Thaiyalnayaki, K. Baskaran, R. Jothi Ramalingam, Hamad Al-Lohedan, Dhaifallah M. Al-dhayan, Muthusamy Karnan, and Kibrom Meansbo Hadish. "Automatic Detection and Segmentation of Colorectal Cancer with Deep Residual Convolutional Neural Network." Evidence-Based Complementary and Alternative Medicine 2022 (March 17, 2022): 1–8. http://dx.doi.org/10.1155/2022/3415603.
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Early and automatic detection of colorectal tumors is essential for cancer analysis, and the same is implemented using computer-aided diagnosis (CAD). A computerized tomography (CT) image of the colon is being used to identify colorectal carcinoma. Digital imaging and communication in medicine (DICOM) is a standard medical imaging format to process and analyze images digitally. Accurate detection of tumor cells in the complex digestive tract is necessary for optimal treatment. The proposed work is divided into two phases. The first phase involves the segmentation, and the second phase is the extraction of the colon lesions with the observed segmentation parameters. A deep convolutional neural network (DCNN) based residual network approach for the colon and polyps’ segmentation from the CT images is applied over the 2D CT images. The residual stack block is being added to the hidden layers with short skip nuance, which helps to retain spatial information. ResNet-enabled CNN is employed in the current work to achieve complete boundary segmentation of the colon cancer region. The results obtained through segmentation serve as features for further extraction and classification of benign as well as malignant colon cancer. Performance evaluation metrics indicate that the proposed network model has effectively segmented and classified colorectal tumors with dice scores of 91.57% (on average), sensitivity = 98.28, specificity = 98.68, and accuracy = 98.82.
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Allen, Carl E. "Acquired Hematologic Disorders of Ras-MAPK Activation." Blood 132, Supplement 1 (November 29, 2018): SCI—42—SCI—42. http://dx.doi.org/10.1182/blood-2018-99-109380.
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Abstract Histiocytic disorders represent a collection of conditions characterized by aberrant function, differentiation and/or proliferation of cells of the mononuclear phagocyte system (MPS). "Histiocyte" is an archaic term (meaning "tissue cell") used to describe phagocytic cells with mononuclear morphology. Clinical approaches to histiocytic disorders have historically been challenged by incomplete understanding of mechanisms of pathogenesis, with debate over classification as cancer versus immune dysregulation. Langerhans cell histiocytosis (LCH), the most common histiocytic disorder, presents with granulomatous lesions with clonal CD207+ dendritic cells (DCs) that can arise as single lesions or life-threatening disseminated disease. Despite the wide range of clinical presentations, LCH lesions are histologically indistinguishable regardless of disease severity. Erdheim-Chester disease (ECD) arises in adults and is characterized by distribution of inflammatory lesions with CD163+ foamy histiocytes involving bone, retroperitoneum/kidney, skin and/or brain. Juvenile xanthogranuloma (JXG), histologically similar to ECD, is typically skin-limited in children, but may also manifest as life-threatening systemic disease. Classical Rosai-Dorfman disease (RDD) presents in children as lymphadenopathy with CD163+ histiocytes and emperipolesis identified in lymph node biopsy, though RDD likely represents a spectrum of conditions in children and adults with a common histiological endpoint. Classification of histiocytic disorders has been based on phenotype of the pathologic cell: LCH (DC-like), non-LCH (macrophage-like), or malignant histiocytosis. BRAFV600E was the first recurrent somatic mutation identified in LCH and ECD. While occurring in 7% of all human cancers, BRAFV600E mutations are also frequently found in benign conditions such as melanocytic nevi and colon polyps. Whole exome sequencing of LCH, ECD, RDD and JXG biopsies has revealed mutually exclusive MAPK pathway activating mutations within otherwise "quiet" genomic landscapes. In LCH, the stage of myeloid differentiation in which the mutation arises defines the extent of disease, and MAPK activation in precursor cells drives myeloid differentiation, blocks migration, and inhibits apoptosis, resulting in accumulation of resilient pathologic DCs that recruit and activate T cells. These new insights support reclassification of these histiocytic disorders as myeloproliferative neoplasms (MPN). Early phase trials in adults with LCH and ECD and emerging pediatric case studies demonstrate promising responses to MAPK pathway inhibitors, though potential for cure and long-term safety are not known. MAPK pathway activation clearly drives pathogenesis in histiocytic MPNs. However, it remains unclear how pathway activation, in many cases with the same mutation in the same CD34+ hematopoietic stem cell, can produce distinct cellular phenotypes with characteristic tissue distribution. While we now are beginning to understand the framework for mechanisms of pathogenesis of histiocytic disorders, continued research will uncover opportunities to identify additional targets and inform personalized therapeutic strategies based on cell of origin, somatic mutation and inherited risk factors. Disclosures No relevant conflicts of interest to declare.
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Kim, Chan-Il, Seok-Min Hwang, Eun-Bin Park, Chang-Hee Won, and Jong-Ha Lee. "Computer-Aided Diagnosis Algorithm for Classification of Malignant Melanoma Using Deep Neural Networks." Sensors 21, no. 16 (August 18, 2021): 5551. http://dx.doi.org/10.3390/s21165551.
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Malignant melanoma accounts for about 1–3% of all malignancies in the West, especially in the United States. More than 9000 people die each year. In general, it is difficult to characterize a skin lesion from a photograph. In this paper, we propose a deep learning-based computer-aided diagnostic algorithm for the classification of malignant melanoma and benign skin tumors from RGB channel skin images. The proposed deep learning model constitutes a tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to classify skin lesions in dermoscopy images. We implement an algorithm to classify malignant melanoma and benign tumors using skin lesion images and expert labeling results from convolutional neural networks. The U-Net model achieved a dice similarity coefficient of 81.1% compared to the expert labeling results. The classification accuracy of malignant melanoma reached 80.06%. As a result, the proposed AI algorithm is expected to be utilized as a computer-aided diagnostic algorithm to help early detection of malignant melanoma.
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Zagrouba, Ezzeddine, and Walid Barhoumi. "A PRELIMARY APPROACH FOR THE AUTOMATED RECOGNITION OF MALIGNANT MELANOMA." Image Analysis & Stereology 23, no. 2 (May 3, 2011): 121. http://dx.doi.org/10.5566/ias.v23.p121-135.
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In this work, we are motivated by the desire to classify skin lesions as malignants or benigns from color photographic slides of the lesions. Thus, we use color images of skin lesions, image processing techniques and artificial neural network classifier to distinguish melanoma from benign pigmented lesions. As the first step of the data set analysis, a preprocessing sequence is implemented to remove noise and undesired structures from the color image. Second, an automated segmentation approach localizes suspicious lesion regions by region growing after a preliminary step based on fuzzy sets. Then, we rely on quantitative image analysis to measure a series of candidate attributes hoped to contain enough information to differentiate melanomas from benign lesions. At last, the selected features are supplied to an artificial neural network for classification of tumor lesion as malignant or benign. For a preliminary balanced training/testing set, our approach is able to obtain 79.1% of correct classification of malignant and benign lesions on real skin lesion images.
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Saman, Faiqa, Safeena Sarfraz, Madiha Arshad, Shahida Niazi, Qurat ul Ain Tahir, and Raheel Ahmed. "HISTOPATHOLOGICAL SPECTRUM OF SKIN ADNEXAL NEOPLASMS AT MAYO HOSPITAL, LAHORE." Pakistan Postgraduate Medical Journal 31, no. 01 (March 27, 2021): 24–28. http://dx.doi.org/10.51642/ppmj.v31i01.383.
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Objective: To find out the frequency of benign and malignant skin adnexal neoplasms.
Study design: Descriptive study.
Materials and methods: It is a descriptive study in which we studied all cases of skin adnexal tumors that are registered in Mayo hospital over the period of six months. Tumors were evaluated according to the type and site , age and gender of the patient. The tumors are classified by WHO classification. Data collected was analyzed using SPSS Version 21 for statistical analysis.
Results: 45 skin adnexal tumors were studied in which, 40 (88.9%) were benign and 5 (11.1%) were malignant.
Conclusion: Skin adnexal tumors are comparatively uncommon lesions. Benign neoplasms are more frequent than malignant.
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Hussain, Muhammad. "Ensemble Classifier for Benign-Malignant Mass Classification." International Journal of Computer Vision and Image Processing 3, no. 1 (January 2013): 66–77. http://dx.doi.org/10.4018/ijcvip.2013010106.
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Mammography is currently the most effective imaging modality for early detection of breast cancer. In a CAD system for masses based on mammography, a mammogram is segmented to detect the masses. The segmentation gives rise to mass regions of interested (ROIs), which are either benign or malignant. There is a need to classify the extracted mass ROIs into benign and malignant masses; it is a hard problem because the texture micro-structures of benign and malignant masses have close resemblance. In this paper, a method for classifying mass ROIs into benign and malignant masses is presented. The key idea of the proposal is to build an ensemble classifier that employs Gabor features, consults different experts (classifiers) and takes the final decision based on majority vote. The system is evaluated on 512 (256 benign+256 malignant) mass ROIs extracted from mammograms of DDSM database. The ensemble classifier improves the classification rate for the problem of the discrimination of benign and malignant masses to 90.64%. Comparison with state-of-the-art techniques suggests that the proposed system outperforms similar methods.
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Pozdnyakova, Viktoria V., Maria I. Maksimova, Liubov Yu Vladimirova, Natalia A. Maksimova, and Yury Valentinovich Przhedetskiy. "Angiogenesis characteristics in benign and malignant melanocytic skin tumors." Journal of Clinical Oncology 36, no. 15_suppl (May 20, 2018): e21529-e21529. http://dx.doi.org/10.1200/jco.2018.36.15_suppl.e21529.
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Kasmi, Reda, and Karim Mokrani. "Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule." IET Image Processing 10, no. 6 (June 1, 2016): 448–55. http://dx.doi.org/10.1049/iet-ipr.2015.0385.
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Gautam, Aman, and Usha Chouhan. "Bio-inspired approaches for classification of benign and malignant tumour of the skin." International Journal of Bioinformatics Research and Applications 17, no. 5 (2021): 424. http://dx.doi.org/10.1504/ijbra.2021.120197.
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Gautam, Aman, and Usha Chouhan. "Bio-inspired approaches for classification of benign and malignant tumour of the skin." International Journal of Bioinformatics Research and Applications 17, no. 5 (2021): 424. http://dx.doi.org/10.1504/ijbra.2021.10043922.
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Hennings, H., R. A. Shores, M. C. Poirier, E. Reed, R. E. Tarone, and S. H. Yuspa. "Enhanced Malignant Conversion of Benign Mouse Skin Tumors by Cisplatin." JNCI Journal of the National Cancer Institute 82, no. 10 (May 16, 1990): 836–40. http://dx.doi.org/10.1093/jnci/82.10.836.
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Gorianto, Frisca Olivia, and I. Gede Santi Astawa. "Breast Cancer Classification Using Artificial Neural Network and Feature Selection." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 8, no. 2 (December 1, 2019): 113. http://dx.doi.org/10.24843/jlk.2019.v08.i02.p01.
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Breast cancer is still one of the leading causes of death in the world. Prevention can be done if the cancer can be recognized early on whether the cancer is malignant or benign. In this study, a comparison of malignant and benign cancer classifications was performed using two artificial neural network methods, which are the Feed-Forward Backpropagation method and the Elman Recurrent Neural Network method, before and after the feature selection of the data. The result of the study produced that Feed-Forward Backpropagation method using 2 hidden layers is better after the feature selection was performed on the data with an accuracy value of 99,26%.
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Zou, Ruiyang, Sau Yeen Loke, Veronique Kiak-Mien Tan, Swee Tian Quek, Pooja Jagmohan, Yew Chung Tang, Preetha Madhukumar, et al. "Development of a microRNA Panel for Classification of Abnormal Mammograms for Breast Cancer." Cancers 13, no. 9 (April 28, 2021): 2130. http://dx.doi.org/10.3390/cancers13092130.
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Mammography is extensively used for breast cancer screening but has high false-positive rates. Here, prospectively collected blood samples were used to identify circulating microRNA (miRNA) biomarkers to discriminate between malignant and benign breast lesions among women with abnormal mammograms. The Discovery cohort comprised 72 patients with breast cancer and 197 patients with benign breast lesions, while the Validation cohort had 73 and 196 cancer and benign cases, respectively. Absolute expression levels of 324 miRNAs were determined using RT-qPCR. miRNA biomarker panels were identified by: (1) determining differential expression between malignant and benign breast lesions, (2) focusing on top differentially expressed miRNAs, and (3) building panels from an unbiased search among all expressed miRNAs. Two-fold cross-validation incorporating a feature selection algorithm and logistic regression was performed. A six-miRNA biomarker panel identified by the third strategy, had an area under the curve (AUC) of 0.785 and 0.774 in the Discovery and Validation cohorts, respectively, and an AUC of 0.881 when differentiating between cases versus those with benign lesions or healthy individuals with normal mammograms. Biomarker panel scores increased with tumor size, stage and number of lymph nodes involved. Our work demonstrates that circulating miRNA signatures can potentially be used with mammography to differentiate between patients with malignant and benign breast lesions.
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Gong, Huiling, Mengjia Qian, Gaofeng Pan, and Bin Hu. "Ultrasound Image Texture Feature Learning-Based Breast Cancer Benign and Malignant Classification." Computational and Mathematical Methods in Medicine 2021 (December 28, 2021): 1–8. http://dx.doi.org/10.1155/2021/6261032.
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The use of ultrasound images to acquire breast cancer diagnosis information without invasion can reduce the physical and psychological pain of breast cancer patients and is of great significance for the diagnosis and treatment of breast cancer. There are some differences in the texture of breast cancer between benign and malignant cases. Therefore, this paper proposes an adaptive learning method based on ultrasonic image texture features to identify breast cancer. Specifically, firstly, we used dictionary learning and sparse representation to learn the ultrasonic image texture dictionary of benign and malignant cases, respectively, and then used the combination of the two dictionaries to represent the test image to obtain the texture distribution characteristics of the test image under the two dictionary representations, which called the sparse representation coefficient. Finally, these above features were filtered by sparse representation and sent to sparse representation classifier to establish benign and malignant classification model. 128 cases were randomly divided into training and testing sets according to 2: 1 for training and testing. The proposed method has achieved state-of-the-art results, with an accuracy of 0.9070 and the area under the receiver operating characteristic curve of 0.9459. The results demonstrate that the proposed method has the potential to be used in the clinical diagnosis of benign and malignant breast cancer.
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Ouyang, Yali, Po-Hsiang Tsui, Shuicai Wu, Weiwei Wu, and Zhuhuang Zhou. "Classification of Benign and Malignant Breast Tumors Using H-Scan Ultrasound Imaging." Diagnostics 9, no. 4 (November 8, 2019): 182. http://dx.doi.org/10.3390/diagnostics9040182.
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Breast cancer is one of the most common cancers among women worldwide. Ultrasound imaging has been widely used in the detection and diagnosis of breast tumors. However, due to factors such as limited spatial resolution and speckle noise, classification of benign and malignant breast tumors using conventional B-mode ultrasound still remains a challenging task. H-scan is a new ultrasound technique that images the relative size of acoustic scatterers. However, the feasibility of H-scan ultrasound imaging in the classification of benign and malignant breast tumors has not been investigated. In this paper, we proposed a new method based on H-scan ultrasound imaging to classify benign and malignant breast tumors. Backscattered ultrasound radiofrequency signals of 100 breast tumors were used (48 benign and 52 malignant cases). H-scan ultrasound images were constructed with the radiofrequency signals by matched filtering using Gaussian-weighted Hermite polynomials. Experimental results showed that benign breast tumors had more red components, while malignant breast tumors had more blue components in H-scan ultrasound images. There were significant differences between the RGB channels of H-scan ultrasound images of benign and malignant breast tumors. We conclude H-scan ultrasound imaging can be used as a new method for classifying benign and malignant breast tumors.
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Muhtadi, Sabiq. "Breast Tumor Classification Using Intratumoral Quantitative Ultrasound Descriptors." Computational and Mathematical Methods in Medicine 2022 (March 7, 2022): 1–18. http://dx.doi.org/10.1155/2022/1633858.
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Breast cancer is a global epidemic, responsible for one of the highest mortality rates among women. Ultrasound imaging is becoming a popular tool for breast cancer screening, and quantitative ultrasound (QUS) techniques are being increasingly applied by researchers in an attempt to characterize breast tissue. Several different quantitative descriptors for breast cancer have been explored by researchers. This study proposes a breast tumor classification system using the three major types of intratumoral QUS descriptors which can be extracted from ultrasound radiofrequency (RF) data: spectral features, envelope statistics features, and texture features. A total of 16 features were extracted from ultrasound RF data across two different datasets, of which one is balanced and the other is severely imbalanced. The balanced dataset contains RF data of 100 patients with breast tumors, of which 48 are benign and 52 are malignant. The imbalanced dataset contains RF data of 130 patients with breast tumors, of which 104 are benign and 26 are malignant. Holdout validation was used to split the balanced dataset into 60% training and 40% testing sets. Feature selection was applied on the training set to identify the most relevant subset for the classification of benign and malignant breast tumors, and the performance of the features was evaluated on the test set. A maximum classification accuracy of 95% and an area under the receiver operating characteristic curve (AUC) of 0.968 was obtained on the test set. The performance of the identified relevant features was further validated on the imbalanced dataset, where a hybrid resampling strategy was firstly utilized to create an optimal balance between benign and malignant samples. A maximum classification accuracy of 93.01%, sensitivity of 94.62%, specificity of 91.4%, and AUC of 0.966 were obtained. The results indicate that the identified features are able to distinguish between benign and malignant breast lesions very effectively, and the combination of the features identified in this research has the potential to be a significant tool in the noninvasive rapid and accurate diagnosis of breast cancer.