Relevant bibliographies by topics / Classification of benign and malignant skin cancer

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Academic literature on the topic 'Classification of benign and malignant skin cancer'

Author: Grafiati
Published: 24 April 2022

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Journal articles on the topic "Classification of benign and malignant skin cancer"

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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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Dissertations / Theses on the topic "Classification of benign and malignant skin cancer"

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Segerström, Pierre, and Felix Boltshauser. "Ensemble Learning Applied to Classification of Malignant and Benign Breast Cancer." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302551.

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In this study, we show how ensemble learning can be useful for the future of breast cancer diagnosis. The chosen ensemble learning method was bagging, which made use of the classifiers Support Vector Machine (SVM), Decision Tree (DT) and Naive Bayes (NB) in order to classify mammograms as benign or malignant. The results achieved with bagging were compared to the results of each individual classifier previously mentioned. Overall, the results showed that the benefits of ensemble learning were varying, dependent on certain factors. Affecting aspects were: which classifier that was used, chosen method for extracting input data, but also which tumor types that were used in training and evaluation of each classifier. While classification using DT improved significantly with bagging, SVM and NB gave negligible performance benefits. Finally, this study only scratched the surface of known ensemble learning methods, indicating that there may be a lot of room for future research in the area.
I denna rapport visar vi hur samlingsinlärning kan vara användbart för framtida diagnostisering av bröstcancer. Den valda samlingsinlärning-metoden var bagging", vilket tog användning av Support Vector Machine (SVM), Decision Tree (DT) och Naive Bayes (NB) för att klassificera mammogram som godartade eller elakartade. Resultaten som togs fram för bagging"jämfördes avslutligen med resultaten från respektive ovannämnd klassifierare. Generellt visade resultaten att fördelarna med samlingsinlärning var varierande, beroende på vissa faktorer. Påverkande aspekter var: vilken klassifierare som användes, vald metod för extraktion av inmatningsdata, men också vilka tumörtyper som användes för träning och evaluering av respektive klassifierare. Medans klassifikation med DT förbättrades signifikant med bagging", var skillnaderna försumbara med SVM och NB. Slutligen, skrapar denna studie enbart på ytan av kända samlingsinlärning-metoder, vilket indikerar att det kan finnas mycket utrymme för framtida forskning i området.
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Books on the topic "Classification of benign and malignant skin cancer"

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MacKie, Rona M. Skin cancer: An illustrated guide to the aetiology, clinical features, pathology and management of benign and malignant cutaneous tumours. London: M. Dunitz, 1989.

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MacKie, Rona M. Skin cancer: An illustrated guide to the aetiology, clinical features, pathology and management of benign and malignant cutaneous tumors. London: Dunitz, 1989.

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Skin Cancer: An Illustrated Guide to the Aetiology, Clinical Features, Pathology and Management of Benign and Malignant Cutaneous Tumours. 2nd ed. Taylor & Francis, 1996.

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Gilmore, Mary Ann. A VALIDATION STUDY FOR THE CLASSIFICATION OF THE LEVEL OF SEVERITY OF MALIGNANT SKIN LESIONS IN CANCER PATIENTS (SKIN CANCER). 1996.

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Sierakowski, Adam, and Roderick Dunn. Skin conditions. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198757689.003.0008.

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This chapter provides an overview of skin conditions affecting the hand, including nail pathology, benign and malignant skin tumours, and Dupuytren’s disease (DD). Although distortion of the nail occurs most commonly after trauma, nail changes may indicate other systemic causes (e.g. psoriasis), and may occasionally be due to underlying malignancy. Hands are exposed to sunlight and other occupational hazards (chemicals, radiation), and are vulnerable to skin cancer, most commonly squamous cell carcinoma. DD is often familial, commoner in men, and can affect the feet (plantar fibromatosis) and penis (Peyronie’s disease). Discreet areas of DD are now treatable by collagenase injection. Surgery is still indicated to restore function, either by fasciectomy (excision of DD) or dermofasciectomy (fasciectomy plus full thickness skin graft) where skin is involved or there is a secondary skin defect following fasciectomy. Patients should be counselled realistically about the post-operative recovery to full function, and that DD is not curable by surgery.
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Lopez-Beltran, Antonio, Rodolfo Montironi, and Liang Cheng. Pathology of renal cancer and other tumours affecting the kidney. Edited by James W. F. Catto. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199659579.003.0085.

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In the past 50 years, classification systems for renal neoplasms have become increasingly complex as distinctive morphologic patterns in renal neoplasms have been recognized and correlated with clinical findings. In addition to classic histopatology, more sophisticated diagnostic tools, including electron microscopy, immunohistochemistry, cytogenetics, and molecular diagnostic techniques have greatly influenced distinctions between various types of renal neoplasms. The current World Health Organization classification of renal neoplasms encompasses nearly 50 distinctive renal neoplasms categorized as malignant or benign tumours. These categories have been expanded during recent years to incorporate newer histotypes, thus suggesting that the next revision of this classification will incorporate some recently recognized entities. In this chapter, we examine clinicopathologic and genetic features of the renal tumours most often seen in clinical practice.
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Gardiner, Matthew D., and Neil R. Borley. Plastic and reconstructive surgery. Oxford University Press, 2012. http://dx.doi.org/10.1093/med/9780199204755.003.0012.

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This chapter begins by discussing the basic principles of acute inflammation, cutaneous wound healing, and the reconstructive ladder, before focusing on the key areas of knowledge, namely congenital conditions, emergency management of burns, emergency hand surgery, tendon injuries, peripheral nerve injuries, elective hand surgery, cutaneous malignant melanoma, non-melanoma skin cancer, and benign skin lesions. The chapter concludes with relevant case-based discussions.
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Book chapters on the topic "Classification of benign and malignant skin cancer"

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Yilmaz, Ercument, and Maria Trocan. "Benign and Malignant Skin Lesion Classification Comparison for Three Deep-Learning Architectures." In Intelligent Information and Database Systems, 514–24. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41964-6_44.

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Napoleon, D., and I. Kalaiarasi. "Classification of Benign and Malignant Lung Cancer Nodule Using Artificial Neural Network." In Intelligent Computing and Innovation on Data Science, 403–11. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3153-5_43.

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Nöhammer, G., F. Bajardi, C. Benedetto, H. Kresbach, W. Rojanapo, E. Schauenstein, and T. F. Slater. "Microphotometric Determination of Protein Thiols and Disulphides in Tissue Samples from the Human Uterine Cervix and the Skin Reveal a “Field Effect” in the Surroundings of Benign and Malignant Tumours." In Eicosanoids, Lipid Peroxidation and Cancer, 291–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73424-3_32.

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O’Toole, Edel. "Tumours of the skin." In Oxford Textbook of Medicine, edited by Roderick J. Hay, 5732–42. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198746690.003.0563.

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A variety of tumours, both benign and malignant, are found in skin. Benign skin lesions, such as seborrhoeic keratoses and skin tags, are often just a cosmetic nuisance, but some benign skin lesions can be a component of diseases with serious medical consequences (e.g. neurofibromatosis or LEOPARD syndrome). Skin cancer is the most common human cancer and its incidence continues to increase. It most commonly affects older, fair-skinned individuals who have had either acute intermittent exposure to ultraviolet light or chronic ultraviolet light exposure. Organ transplant recipients have a 200-fold increased risk of squamous cell carcinoma. About 2% of patients who develop skin cancer have a genetic predisposition, for example, Gorlin’s syndrome in basal cell carcinoma and familial melanoma syndromes in malignant melanoma. Mutations in the PTCH gene cause Gorlin’s syndrome, and loss of heterozygosity at that locus is also present in most sporadic basal cell carcinoma.
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C, Geetha, Aparna Darapaneni, and Lakkamaneni Chandana Manaswini. "Intelligent Systems to Predict and Diagnose Benign and Malignant Skin Lesions." In Intelligent Systems and Computer Technology. IOS Press, 2020. http://dx.doi.org/10.3233/apc200150.

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The main purpose of Intelligent systems is to reason, calculate and perceive relationships and analogies. These Intelligent systems learn from experience and retrieve information from memory and provide the same to the users based onss their requirement. Currently, there is a trend for the use of intelligent systems in health informatics. The main objective of this is to improve quality, efficiency and availability of health services to people round the clock at a lower cost. Intelligent systems aim to predict and diagnose the skin cancer and abrasions based on their images. It understands the cause and thereby analyses the image based on some of the image processing techniques like patterns, anisotropic diffusion, image editing, independent component analysis and image restoration. We make use of image processing software which captures the image and then converts it to digital form and perform the required manipulations.
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Ben Youssef, Youssef, Elhassane Abdelmounim, and Abdelaziz Belaguid. "Mammogram Classification Using Support Vector Machine." In Cognitive Analytics, 894–921. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch046.

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Among the objectives of artificial intelligence techniques, we find computer-aided diagnosis systems that support preventive medical check-ups and perform detection, recognition, and classification patterns. Recently these techniques are emerged in different areas particularly in medical imaging. Medical image is an important source of information, and a golden tool for the diagnosis and assessment of a pathological analysis process. In this chapter Computer-Aided Diagnosis (CAD) system is proposed in detection and diagnosis of breast cancer, it is mainly composed of the following steps: preprocessing mammographic image, segmentation of suspect region on the mammographic image using Chan Vese model, extraction of global and local descriptors and then image classification into malignant and benign mammograms using Support Vector Machine (SVM) classifier. The analysis of mammographic images proposed system with a choice of the subset of local descriptors after tumor segmentation leads to a classification of malignant and benign mammograms. System proposed achieves 92% for accuracy.
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Ben Youssef, Youssef, Elhassane Abdelmounim, and Abdelaziz Belaguid. "Mammogram Classification Using Support Vector Machine." In Advances in Wireless Technologies and Telecommunication, 587–614. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0773-4.ch019.

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Among the objectives of artificial intelligence techniques, we find computer-aided diagnosis systems that support preventive medical check-ups and perform detection, recognition, and classification patterns. Recently these techniques are emerged in different areas particularly in medical imaging. Medical image is an important source of information, and a golden tool for the diagnosis and assessment of a pathological analysis process. In this chapter Computer-Aided Diagnosis (CAD) system is proposed in detection and diagnosis of breast cancer, it is mainly composed of the following steps: preprocessing mammographic image, segmentation of suspect region on the mammographic image using Chan Vese model, extraction of global and local descriptors and then image classification into malignant and benign mammograms using Support Vector Machine (SVM) classifier. The analysis of mammographic images proposed system with a choice of the subset of local descriptors after tumor segmentation leads to a classification of malignant and benign mammograms. System proposed achieves 92% for accuracy.
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Fatima, Kiran, and Hammad Majeed. "Texture-Based Evolutionary Method for Cancer Classification in Histopathology." In Advances in Data Mining and Database Management, 55–69. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9767-6.ch004.

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Real-world histology tissue textures owing to non-homogeneous nature and unorganized spatial intensity variations are complex to analyze and classify. The major challenge in solving pathological problems is inherent complexity due to high intra-class variability and low inter-class variation in texture of histology samples. The development of computational methods to assists pathologists in characterization of these tissue samples would have great diagnostic and prognostic value. In this chapter, an optimized texture-based evolutionary framework is proposed to provide assistance to pathologists for classification of benign and pre-malignant tumors. The proposed framework investigates the imperative role of RGB color channels for discrimination of cancer grades or subtypes, explores higher-order statistical features at image-level, and implements an evolution-based optimization scheme for feature selection and classification. The highest classification accuracy of 99.06% is achieved on meningioma dataset and 90% on breast cancer dataset through Quadratic SVM classifier.
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Fatima, Kiran, and Hammad Majeed. "Texture-Based Evolutionary Method for Cancer Classification in Histopathology." In Medical Imaging, 558–72. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0571-6.ch021.

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Real-world histology tissue textures owing to non-homogeneous nature and unorganized spatial intensity variations are complex to analyze and classify. The major challenge in solving pathological problems is inherent complexity due to high intra-class variability and low inter-class variation in texture of histology samples. The development of computational methods to assists pathologists in characterization of these tissue samples would have great diagnostic and prognostic value. In this chapter, an optimized texture-based evolutionary framework is proposed to provide assistance to pathologists for classification of benign and pre-malignant tumors. The proposed framework investigates the imperative role of RGB color channels for discrimination of cancer grades or subtypes, explores higher-order statistical features at image-level, and implements an evolution-based optimization scheme for feature selection and classification. The highest classification accuracy of 99.06% is achieved on meningioma dataset and 90% on breast cancer dataset through Quadratic SVM classifier.
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Sharma, Neha, and Deepti Sharma. "An Overview of Pancreatic Neuroendocrine Tumors." In Challenges in Pancreatic Cancer. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.96259.

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Pancreatic neuroendocrine tumors are a group of endocrine tumors that constitute 7% of all pancreatic neoplasms. They can be benign or malignant. Their presentation can vary from slow growing, non infiltrative, indolent masses to rapidly progressing, highly aggressive, metastasizing tumors. In the past, there was paucity of scientific data available about the diagnosis and treatment strategy of these neoplasms but in recent years, ongoing research has inferred much data regarding classification, prognostic stratification and therapy of pancreatic neuroendocrine tumors. In this chapter we will discuss epidemiology, clinical presentation and classification, diagnosis and management of these tumors. We will also deliberate about the latest developments in treatment of pancreatic neuroendocrine tumors with focus on recent studies done on this topic.
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Conference papers on the topic "Classification of benign and malignant skin cancer"

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Alamdari, Nasim, Nicholas MacKinnon, Fartash Vasefi, Reza Fazel-Rezai, Minhal Alhashim, Alireza Akhbardeh, Daniel L. Farkas, and Kouhyar Tavakolian. "Effect of Lesion Segmentation in Melanoma Diagnosis for a Mobile Health Application." In 2017 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dmd2017-3522.

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In 2016, more than 76,380 new melanoma cases were diagnosed and 10,130 people were expected to die from skin cancer in the United States (one death per hour) [1]. A recent study demonstrates that the economic burden of skin cancer treatment is substantial and, in the United States, the cost was increased from $3.6 billion in 2002–2006 to $8.1 billion in 2007–2011 [2]. Monitoring moderate and high-risk patients and identifying melanoma in the earliest stage of disease should save lives and greatly diminish the cost of treatment. In this project, we are focused on detection and monitoring of new potential melanoma sites with medium/high risk patients. We believe those patients have a serious need and they need to be motivated to be engaged in their treatment plan. High-risk patients are more likely to be engaged with their skin health and their health care providers (physicians). Considering the high morbidity and mortality of melanoma, these patients are motivated to spend money on low-cost mobile device technology, either from their own pocket or through their health care provider if it helps reduce their risk with early detection and treatment. We believe that there is a role for mobile device imaging tools in the management of melanoma risk, if they are based on clinically validated technology that supports the existing needs of patients and the health care system. In a study issued in the British Journal of Dermatology [2] of 39 melanoma apps [2], five requested to do risk assessment, while nine mentioned images for expert review. The rest fell into the documentation and education categories. This seems like to be reliable with other dermatology apps available on the market. In a study at University of Pittsburgh [3], Ferris et al. established 4 apps with 188 clinically validated skin lesions images. From images, 60 of them were melanomas. Three of four apps tested misclassified +30% of melanomas as benign. The fourth app was more accurate and it depended on dermatologist interpretation. These results raise questions about proper use of smartphones in diagnosis and treatment of the patients and how dermatologists can effectively involve with these tools. In this study, we used a MATLAB (The MathWorks Inc., Natick, MA) based image processing algorithm that uses an RGB color dermoscopy image as an input and classifies malignant melanoma versus benign lesions based on prior training data using the AdaBoost classifier [5]. We compared the classifier accuracy when lesion boundaries are detected using supervised and unsupervised segmentation. We have found that improving the lesion boundary detection accuracy provides significant improvement on melanoma classification outcome in the patient data.
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Hossain, Milon, Kh Sadik, Md Musfiqur Rahman, Fahad Ahmed, Md Nur Hossain Bhuiyan, and Mohammad Monirujjaman Khan. "Convolutional Neural Network Based Skin Cancer Detection (Malignant vs Benign)." In 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2021. http://dx.doi.org/10.1109/iemcon53756.2021.9623192.

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Ara, Sharmin, Annesha Das, and Ashim Dey. "Malignant and Benign Breast Cancer Classification using Machine Learning Algorithms." In 2021 International Conference on Artificial Intelligence (ICAI). IEEE, 2021. http://dx.doi.org/10.1109/icai52203.2021.9445249.

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Jiang, Zhiqiang, Weidong Xu, and Shujun Chen. "Classification of benign and malignant breast cancer based on DWI texture features." In the International Conference. New York, New York, USA: ACM Press, 2017. http://dx.doi.org/10.1145/3135954.3135964.

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Anparasy, S. "Classification of Breast cancer tumors using Feature Selection and CNN." In ERU Symposium 2021. Engineering Research Unit (ERU), University of Moratuwa, 2021. http://dx.doi.org/10.31705/eru.2021.11.

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Breast cancer is one of the most dangerous diseases in the world and almost two million new cases are diagnosed every year. It starts from the breasts tissue and then spreads to other parts of the body. Early detection of breast cancer is important to save the life of a woman as it is related with a risen number of available treatment options. Benign and malignant are the major types of tumors and they are cancerous and non-cancerous, respectively. Benign is not dangerous since it does not destroy the nearby tissues and cannot spread or grow. Malignant tumor invades neighbouring tissues, blood vessels and spreads to other parts of the body by metastasis. Therefore, differentiating malignant from benign will help to detect breast cancer in its early stage. Nowadays, machine learning techniques are used to classify the tumor types hence the quality of lift is increased.
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Mahmoud, Mohamed Khalad Abu, Adel Al-Jumaily, Yashar Maali, and Khairul Anam. "Classification of Malignant Melanoma and Benign Nevi from Skin Lesions Based on Support Vector Machine." In 2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation (CIMSim). IEEE, 2013. http://dx.doi.org/10.1109/cimsim.2013.45.

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Babaghorbani, P., S. Parvaneh, AR Ghassemi, and K. Manshai. "Sonography Images for Breast Cancer Texture Classification in Diagnosis of Malignant or Benign Tumors." In 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2010. http://dx.doi.org/10.1109/icbbe.2010.5516073.

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Rajesh, A. "Classification of malignant melanoma and Benign Skin Lesion by using back propagation neural network and ABCD rule." In 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE). IEEE, 2017. http://dx.doi.org/10.1109/iceice.2017.8191916.

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Eshun, Robert B., A. K. M. Kamrul Islam, and Marwan U. Bikdash. "Identification of Significantly Expressed Gene Mutations for Automated Classification of Benign and Malignant Prostate Cancer." In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021. http://dx.doi.org/10.1109/embc46164.2021.9630460.

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Shia, Weichung, and Darren Chen. "Abstract P1-02-10: Using deep residual networks for malignant and benign classification of two-dimensional Doppler breast ultrasound imaging." In Abstracts: 2019 San Antonio Breast Cancer Symposium; December 10-14, 2019; San Antonio, Texas. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.sabcs19-p1-02-10.

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