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Articoli di riviste sul tema "Automated Cancer Diagnosis"

Autore: Grafiati

Pubblicato: 4 giugno 2025

Ultima modifica: 1 agosto 2025

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1

Singh, Seema, V. Tejaswini, Rishya P. Murthy, and Amit Mutgi. "Neural Network Based Automated System for Diagnosis of Cervical Cancer." International Journal of Biomedical and Clinical Engineering 4, no. 2 (2015): 26–39. http://dx.doi.org/10.4018/ijbce.2015070103.

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Cervical Cancer is one of the most common cancers among women worldwide. Few concerns have arisen such as the shortage of skilled pathologists leading to increase in burden on them. This requires a need for efficient and accurate method that diagnoses cervical cancer without human intervention. In this paper, an automated system is developed for diagnosis of cervical cancer using image processing techniques and neural networks. The system is developed using Cytology images taken from Bangalore based cancer pathologist. MATLAB image processing toolbox is used to extract features from cytology i

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N., Gangatharan, Muthumanickam S., Rajagopal R., Sudharsanam V., Sai Rakesh K. V., and Sanjive Kumaran B. "Enhancing Skin Cancer Diagnosis with Deep Learning-Based Classification." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (2023): 105–11. http://dx.doi.org/10.17762/ijritcc.v11i5s.6634.

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The diagnosis of skin cancer has been identified as a significant medical challenge in the 21st century due to its complexity, cost, and subjective interpretation. Early diagnosis is critical, especially in fatal cases like melanoma, as it affects the likelihood of successful treatment. Therefore, there is a need for automated methods in early diagnosis, especially with a diverse range of image samples with varying diagnoses. An automated system for dermatological disease recognition through image analysis has been proposed and compared to conventional medical personnel-based detection. This p

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Gurumoorthy, R., and M. Kamarasan. "Automated Breast Cancer Diagnosis using Optimization Algorithm with Deep Learning on Histopathological Images." International Journal of Science and Research (IJSR) 12, no. 12 (2023): 1506–12. http://dx.doi.org/10.21275/sr231218172554.

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Demir, C., S. H. Gultekin, and B. Yener. "Augmented cell-graphs for automated cancer diagnosis." Bioinformatics 21, Suppl 2 (2005): ii7—ii12. http://dx.doi.org/10.1093/bioinformatics/bti1100.

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Min, Jouha, Lip Ket Chin, Juhyun Oh, et al. "CytoPAN—Portable cellular analyses for rapid point-of-care cancer diagnosis." Science Translational Medicine 12, no. 555 (2020): eaaz9746. http://dx.doi.org/10.1126/scitranslmed.aaz9746.

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Rapid, automated, point-of-care cellular diagnosis of cancer remains difficult in remote settings due to lack of specialists and medical infrastructure. To address the need for same-day diagnosis, we developed an automated image cytometry system (CytoPAN) that allows rapid breast cancer diagnosis of scant cellular specimens obtained by fine needle aspiration (FNA) of palpable mass lesions. The system is devoid of moving parts for stable operations, harnesses optimized antibody kits for multiplexed analysis, and offers a user-friendly interface with automated analysis for rapid diagnoses. Throu

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Alharbi, Abir. "An Automated Computer System Based on Genetic Algorithm and Fuzzy Systems for Lung Cancer Diagnosis." International Journal of Nonlinear Sciences and Numerical Simulation 19, no. 6 (2018): 583–94. http://dx.doi.org/10.1515/ijnsns-2017-0048.

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AbstractAn automated system for the diagnosis of lung cancer is proposed in this paper, the system is designed by combining two major methodologies, namely the fuzzy base systems and the evolutionary genetic algorithms (GAs), to be employed on lung cancer data to assist physicians in the early detection of lung cancers, and hence obtain an early automated diagnosis complementary to that by physicians. Our hybrid algorithm, the genetic-fuzzy algorithm, has produced optimized diagnosis systems that attain high classification performance, in fact, our best six rule system obtained a 97.5 % accura

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Altunbay, D., C. Cigir, C. Sokmensuer, and C. Gunduz-Demir. "Color Graphs for Automated Cancer Diagnosis and Grading." IEEE Transactions on Biomedical Engineering 57, no. 3 (2010): 665–74. http://dx.doi.org/10.1109/tbme.2009.2033804.

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Raphael, A. P., and H. P. Soyer. "Automated diagnosis: shedding the light on skin cancer." British Journal of Dermatology 178, no. 2 (2018): 331–33. http://dx.doi.org/10.1111/bjd.16219.

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Beaulieu, Robert J., Seth D. Goldstein, Jasvinder Singh, Bashar Safar, Amit Banerjee, and Nita Ahuja. "Automated diagnosis of colon cancer using hyperspectral sensing." International Journal of Medical Robotics and Computer Assisted Surgery 14, no. 3 (2018): e1897. http://dx.doi.org/10.1002/rcs.1897.

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Dr., Monalisa Hati and Safa Asif Dabir. "Implementing Deep Learning in Automated Blood Cancer Diagnosis." Academic 2, no. 10 (2024): 770–79. https://doi.org/10.5281/zenodo.14106657.

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Grace, Olusunde T., and Ernest E. Onuiri. "Machine Learning-based Automated Diagnosis for Cervical Cancer Using Pap Smear Images: A Systematic Review." International Journal of Research Publication and Reviews 5, no. 9 (2024): 1086–92. http://dx.doi.org/10.55248/gengpi.5.0924.2517.

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Ghosh, Sayani, Sayantan Dey, and Souvik Chatterjee. "Automated Breast Cancer Diagnosis Based on Machine Learning Algorithm." American Journal of Electronics & Communication 2, no. 1 (2021): 4–9. http://dx.doi.org/10.15864/ajec.2102.

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Abstract – Breast Cancer classification is becoming more important with the increasing demand of automated applications especially interactive applications. It can be used to improve the performance of classifiers like Logistic Regression, Decision Tree, Random Forest, SVC etc. This study is based on learning genetic patterns of patients with breast tumors and machine learning algorithms that aim to demonstrate a system to accurately differentiate between benign and malignant breast tumors. The aim of this study was to optimize different algorithm. In this context, we applied the genetic progr

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TİRYAKİ, Volkan Müjdat. "Deep Transfer Learning to Classify Mass and Calcification Pathologies from Screen Film Mammograms." Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 12, no. 1 (2023): 57–65. http://dx.doi.org/10.17798/bitlisfen.1190134.

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The number of breast cancer diagnosis is the biggest among all cancers, but it can be treated if diagnosed early. Mammography is commonly used for detecting abnormalities and diagnosing the breast cancer. Breast cancer screening and diagnosis are still being performed by radiologists. In the last decade, deep learning was successfully applied on big image classification databases such as ImageNet. Deep learning methods for the automated breast cancer diagnosis is under investigation. In this study, breast cancer mass and calcification pathologies are classified by using deep transfer learning

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Alsultan, Safa Amer Baker. "Deep Learning-Based Automated Diagnosis of Skin Cancer from Thermoscopic Images." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 32, no. 4 (2024): 199–212. https://doi.org/10.29196/jubpas.v32i4.5533.

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Background: Given the importance of early and accurate detection of skin cancer in preventing its risks and improving its treatment, the study aims to develop automated models based on deep learning to detect skin cancer and accurately classify skin lesions into benign and malignant and identify different types of lesions, which helps doctors in early diagnosis and decision-making. Materials and Methods: Relying on pre-trained convolutional neural networks, which makes it possible to train models using only a small amount of available training data. Two datasets of images of skin tumors were u

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Singh, Bhanu Pratap, and Rupashri Barik. "Image Segmentation Based Automated Skin Cancer Detection Technique." Indian Journal of Image Processing and Recognition 3, no. 5 (2023): 1–6. http://dx.doi.org/10.54105/ijipr.h9682.083523.

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Skin cancer is a prevalent and deadly disease that affects millions of people worldwide. Early detection and diagnosis of skin cancer can significantly improve the chances of successful treatment and recovery. This study proposes a skin cancer segmentation and detection system using image processing and deep learning techniques to automate the diagnosis process. The system is trained on a dataset of skin images and uses a deep learning algorithm to classify skin lesions as benign or malignant. The performance of the system is evaluated using various metrics, including accuracy, precision, reca

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Khan, Huda, Anushka Yadav, Reha Santiago, and Sangita Chaudhari. "Automated Non-invasive Diagnosis of Melanoma Skin Cancer using Dermo-scopic Images." ITM Web of Conferences 32 (2020): 03029. http://dx.doi.org/10.1051/itmconf/20203203029.

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Melanoma skin cancer is one of the deadliest cancers today, the rate of which is rising exponentially. If not detected and treated early, it will most likely spread to other parts of the body. To properly detect melanoma, a skin biopsy is required. This is an invasive technique which is why the need for a diagnosis system that can eradicate the skin biopsy method arises. It is observed that the proposed method is successfully detecting and correctly classifying the malignant and non-malignant skin cancer. Finally, a neural network is used to classify benign and malignant images from the extrac

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Melder, Karl K., and Leopold G. Koss. "Automated image analysis in the diagnosis of bladder cancer." Applied Optics 26, no. 16 (1987): 3367. http://dx.doi.org/10.1364/ao.26.003367.

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Dhahri, Habib, Eslam Al Maghayreh, Awais Mahmood, Wail Elkilani, and Mohammed Faisal Nagi. "Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms." Journal of Healthcare Engineering 2019 (November 3, 2019): 1–11. http://dx.doi.org/10.1155/2019/4253641.

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There have been several empirical studies addressing breast cancer using machine learning and soft computing techniques. Many claim that their algorithms are faster, easier, or more accurate than others are. This study is based on genetic programming and machine learning algorithms that aim to construct a system to accurately differentiate between benign and malignant breast tumors. The aim of this study was to optimize the learning algorithm. In this context, we applied the genetic programming technique to select the best features and perfect parameter values of the machine learning classifie

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Angel Arul Jothi, J., and V. Mary Anita Rajam. "A survey on automated cancer diagnosis from histopathology images." Artificial Intelligence Review 48, no. 1 (2016): 31–81. http://dx.doi.org/10.1007/s10462-016-9494-6.

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Choudhary, Satish, Priyanka Singh, Mann Mittal, and Gaurav Singh. "Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms." International journal Advanced Networking and Applications 15, no. 06 (2024): 6229–38. http://dx.doi.org/10.35444/ijana.2024.15610.

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Lazarev, A. F., Valentina D. Petrova, V. P. Pokornyak, S. A. Lazarev, V. A. Marchkov, and D. I. Ganov. "Digital technologies in prostate cancer prevention and early diagnosis." Russian Journal of Oncology 25, no. 6 (2020): 192–99. http://dx.doi.org/10.17816/1028-9984-2020-25-6-192-199.

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Background. Prostate cancer is one of the most common malignant neoplasms in males. The Russian Federation observed the same patterns: the incidence of prostate cancer is steadily growing, without the tendency to decrease. Currently, no effective methods are available for prostate cancer early diagnosis and screening. Aim. To improve the effectiveness of prostate cancer prevention and early diagnosis with new digital technologies for high-risk cancer group formation Material and methods. Data from the Cancer Registry population in the Altai regional cancer center, Barnaul City was

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Lazarev, A. F., Valentina D. Petrova, V. P. Pokornyak, S. A. Lazarev, V. A. Marchkov, and D. I. Ganov. "Digital technologies in prostate cancer prevention and early diagnosis." Russian Journal of Oncology 25, no. 6 (2020): 192–99. http://dx.doi.org/10.17816/1028-9984-2020-25-6-192-199.

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Background. Prostate cancer is one of the most common malignant neoplasms in males. The Russian Federation observed the same patterns: the incidence of prostate cancer is steadily growing, without the tendency to decrease. Currently, no effective methods are available for prostate cancer early diagnosis and screening. Aim. To improve the effectiveness of prostate cancer prevention and early diagnosis with new digital technologies for high-risk cancer group formation Material and methods. Data from the Cancer Registry population in the Altai regional cancer center, Barnaul City was

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Hasan, Imran, Shahin Ali, Habibur Rahman, and Khairul Islam. "Automated Detection and Characterization of Colon Cancer with Deep Convolutional Neural Networks." Journal of Healthcare Engineering 2022 (August 24, 2022): 1–12. http://dx.doi.org/10.1155/2022/5269913.

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Colon cancer is a momentous reason for illness and death in people. The conclusive diagnosis of colon cancer is made through histological examination. Convolutional neural networks are being used to analyze colon cancer via digital image processing with the introduction of whole-slide imaging. Accurate categorization of colon cancers is necessary for capable analysis. Our objective is to promote a system for detecting and classifying colon adenocarcinomas by applying a deep convolutional neural network (DCNN) model with some preprocessing techniques on digital histopathology images. It is a le

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Khan, Irfan Ullah, Nida Aslam, Talha Anwar, et al. "Remote Diagnosis and Triaging Model for Skin Cancer Using EfficientNet and Extreme Gradient Boosting." Complexity 2021 (September 9, 2021): 1–13. http://dx.doi.org/10.1155/2021/5591614.

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Due to the successful application of machine learning techniques in several fields, automated diagnosis system in healthcare has been increasing at a high rate. The aim of the study is to propose an automated skin cancer diagnosis and triaging model and to explore the impact of integrating the clinical features in the diagnosis and enhance the outcomes achieved by the literature study. We used an ensemble-learning framework, consisting of the EfficientNetB3 deep learning model for skin lesion analysis and Extreme Gradient Boosting (XGB) for clinical data. The study used PAD-UFES-20 data set co

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Melarkode, Navneet, Kathiravan Srinivasan, Saeed Mian Qaisar, and Pawel Plawiak. "AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions." Cancers 15, no. 4 (2023): 1183. http://dx.doi.org/10.3390/cancers15041183.

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Skin cancer continues to remain one of the major healthcare issues across the globe. If diagnosed early, skin cancer can be treated successfully. While early diagnosis is paramount for an effective cure for cancer, the current process requires the involvement of skin cancer specialists, which makes it an expensive procedure and not easily available and affordable in developing countries. This dearth of skin cancer specialists has given rise to the need to develop automated diagnosis systems. In this context, Artificial Intelligence (AI)-based methods have been proposed. These systems can assis

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Painuly, Sakshi. "Machine Learning-based Automated Diagnosis of Breast Cancer from Mammography Images." Mathematical Statistician and Engineering Applications 70, no. 2 (2021): 1811–21. http://dx.doi.org/10.17762/msea.v70i2.2474.

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This paper presents a novel machine learning-based system for the automated diagnosis of breast cancer using mammography images. The proposed system employs a nature-inspired feature extraction algorithm to accurately identify and highlight the salient features within the mammographic scans. The features derived are highly representative and effective in distinguishing between malignant and benign cases, thus addressing the inherent complexity and variability within the breast tissue structures. To enhance the prediction accuracy, a hybrid decision tree and gradient boosting algorithm is intro

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Zhou, Jade, Shelly Kane, Carl Curtis, et al. "Improving documentation of cancer staging at an academic cancer center utilizing an EMR-based system with public accountability." Journal of Clinical Oncology 38, no. 29_suppl (2020): 197. http://dx.doi.org/10.1200/jco.2020.38.29_suppl.197.

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197 Background: Accurate TNM staging of malignancy is essential to quality care of cancer patients but maintaining consistent documentation of appropriate staging remains a challenge. We identified documentation of TNM staging at our institution to be below target levels. We sought to improve documentation of staging in all patients at our cancer center with a diagnosis of malignancy by implementing both automated and manual reminders through our electronic medical record (EMR) software (Epic), as well as by using team accountability. Methods: We defined an expectation that all patients seen a

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Shaikh, Imran, and Kadam V.K. "Automatic Computer Propped Diagnosis Framework of Liver Cancer Detection with Simulation using CNN LSTM." International Journal of Engineering Research in Electrical and Electronics Engineering 9, no. 1 (2022): 1–7. http://dx.doi.org/10.36647/ijereee/09.01.a001.

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Initial prediction of any kind of cancer is always advantageous for on-time medical treatment to save the patient's life. The Computer-Aided Diagnosis (CAD) tools using signal processing & image processing methods gained significant attention for immediate & accurate diagnosis using patient’s raw medical data like Magnetic Resonance Imaging (MRI), Chromatography (CT), etc. The liver cancer early detection & analysis of its grading is an important research problem. In this research, we proposed the two models semi-automatic & automatic frameworks for liver disease classification

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Zuo, Wenji. "Deep learning for enhancing cancer identification and diagnosis." Applied and Computational Engineering 27, no. 1 (2023): 199–205. http://dx.doi.org/10.54254/2755-2721/27/20230093.

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One of the promising and significant fields of technology is the use of automated computer techniques, particularly machine learning, to facilitate and enhance medical analysis and diagnosis. In the area of artificial intelligence (AI), deep learning techniques using artificial neural networks (CNNs) so-called because they superficially resemble biological neural networks - are computational network models for discovering large, high-dimensional data sets (such as medical datasets) for complex structures and patterns. In this paper, the focus is on summarizing contemporary applications of vari

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Anand, S. "Biosensors for early diagnosis and automated drug delivery in pancreatic cancer." i-manager’s Journal on Embedded Systems 12, no. 2 (2024): 32. http://dx.doi.org/10.26634/jes.12.2.20958.

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Pancreatic cancer remains one of the most challenging malignancies to diagnose and treat effectively, resulting in poor patient outcomes due to late-stage detection and limited therapeutic options. The emergence of biosensors has revolutionized cancer diagnosis and therapy, providing new avenues for early detection and personalized treatment. This paper explores the development and integration of biosensors within a unique expert system for pancreatic cancer diagnosis and drug delivery automation. It discusses the principles, types, and applications of biosensors in pancreatic cancer diagnosis

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Kanimozhi, G., and P. Shanmugavadivu. "OPTIMIZED DEEP NEURAL NETWORKS ARCHITECTURE MODEL FOR BREAST CANCER DIAGNOSIS." YMER Digital 20, no. 11 (2021): 161–75. http://dx.doi.org/10.37896/ymer20.11/15.

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Breast cancer has increasingly claimed the lives of women. Oncologists use digital mammograms as a viable source to detect breast cancer and classify it into benign and malignant based on the severity. The performance of the traditional methods on breast cancer detection could not be improved beyond a certain point due to the limitations and scope of computing. Moreover, the constrained scope of image processing techniques in developing automated breast cancer detection systems has motivated the researchers to shift their focus towards Artificial Intelligence based models. The Neural Networks

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Menzies, S. W., W. H. McCarthy, B. Crook, et al. "Automated instrumentation for the diagnosis of invasive melanoma." Melanoma Research 6, SUPPLEMENT 1 (1996): S47. http://dx.doi.org/10.1097/00008390-199609001-00118.

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Menzies, S. W., B. Crook, W. H. McCarthy, et al. "Automated instrumentation for the diagnosis of invasive melanoma." Melanoma Research 7, Supplement 1 (1997): S13. http://dx.doi.org/10.1097/00008390-199706001-00042.

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Babarenda Gamage, Thiranja Prasad, Duane T. K. Malcolm, Gonzalo Maso Talou, et al. "An automated computational biomechanics workflow for improving breast cancer diagnosis and treatment." Interface Focus 9, no. 4 (2019): 20190034. http://dx.doi.org/10.1098/rsfs.2019.0034.

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Clinicians face many challenges when diagnosing and treating breast cancer. These challenges include interpreting and co-locating information between different medical imaging modalities that are used to identify tumours and predicting where these tumours move to during different treatment procedures. We have developed a novel automated breast image analysis workflow that integrates state-of-the-art image processing and machine learning techniques, personalized three-dimensional biomechanical modelling and population-based statistical analysis to assist clinicians during breast cancer detectio

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Hudson, Sue M., Louise S. Wilkinson, Bianca L. De Stavola, and Isabel dos-Santos-Silva. "Left–right breast asymmetry and risk of screen-detected and interval cancers in a large population-based screening population." British Journal of Radiology 93, no. 1112 (2020): 20200154. http://dx.doi.org/10.1259/bjr.20200154.

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Objectives: To assess the associations between automated volumetric estimates of mammographic asymmetry and breast cancers detected at the same (“contemporaneous”) screen, at subsequent screens, or in between (interval cancers). Methods: Automated measurements from mammographic images (N = 79,731) were used to estimate absolute asymmetry in breast volume (BV) and dense volume (DV) in a large ethnically diverse population of attendees of a UK breast screening programme. Logistic regression models were fitted to assess asymmetry associations with the odds of a breast cancer detected at contempor

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Barchuk, Anton, A. Atroshchenko, V. Gaydukov, et al. "AUTOMATED DIAGNOSIS IN A POPULATION-BASED SCREENING FOR LUNG CANCER." Problems in oncology 63, no. 2 (2017): 215–20. http://dx.doi.org/10.37469/0507-3758-2017-63-2-215-220.

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Oncologists nowadays are faced with big amount of heterogeneous medical data of diagnostic studies. Possible errors in determining the nature and extent of spread the tumor process will inevitably reduce the effectiveness of treatment and increase the unnecessary costs to it. To reduce the burden on clinicians, various computer-aided solutions based on machine learning algorithms are being developed. We made an attempt to evaluate effectiveness of thirteen machine learning algorithms in the tasks of classification of pathologic tissue samples in cancerous thorax based on gene expression levels

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Ozdemir, E., C. Sokmensuer, and C. Gunduz-Demir. "A Resampling-Based Markovian Model for Automated Colon Cancer Diagnosis." IEEE Transactions on Biomedical Engineering 59, no. 1 (2012): 281–89. http://dx.doi.org/10.1109/tbme.2011.2173934.

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Perez, Gustavo, and Pablo Arbelaez. "Automated lung cancer diagnosis using three-dimensional convolutional neural networks." Medical & Biological Engineering & Computing 58, no. 8 (2020): 1803–15. http://dx.doi.org/10.1007/s11517-020-02197-7.

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Ikhlas, Ahmad Lone*1& Manmeen Kaur2. "A COMPREHENSIVE SURVEY ON AUTOMATIC SEGMENTATION AND CLASSIFICATION OF SKIN CANCER FROM DERMOSCOPIC IMAGES." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 9, no. 3 (2020): 13–19. https://doi.org/10.5281/zenodo.3700409.

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Skin cancer is defined as a preventive disease that spreads across the bloodstream of the human body. There is no possibility or methods to find out the exact reason for any type of cancer, but here is the cause of cancer such as tobacco consumption, bad diet, absence of physical activity, obesity, UV exposure and alcohol consumption. Malignant type of skin cancer is the deadliest form of skin cancer. It can be easily treatable if detected in early stages. Clinical as well as automated methods are being used for skin cancer diagnosis but computer aided diagnosis systems have great potential fo

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Žaja, Marija, and Tatjana Matijaš. "Automated Breast Ultrasound." Radiološki vjesnik 46, no. 2 (2022): 10–17. http://dx.doi.org/10.55378/rv.46.2.2.

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Due to the growing number of breast cancer patients, an early diagnosis is important in order to reduce the mortality rate of those affected. Methods such as mammography, DBT, MRI, HHUS or ABUS are used in the detection of breast cancer. The aim of this article is to review the literature showing the basic principle of ABUS and to point out its advantages and disadvantages in relation to conventional methods of breast imaging. ABUS is a relatively new ultrasound method that performs well on patients with dense breast tissue. It reduces operator dependence and provides valuable diagnostic infor

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Shaikh, Imran, and Kadam V.K. "Automatic Computer Propped Diagnosis Framework of Liver Cancer Detection using CNN LSTM." International Journal of Engineering Research in Electronics and Communication Engineering 9, no. 2 (2022): 1–8. http://dx.doi.org/10.36647/ijerece/09.02.a001.

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Liver cancer detection using the computer vision methods and machine learning already received significant attention of researchers for authentic diagnosis and on-time medical attentions. The Computer Aided Diagnosis (CAD) preferred for cancer detection all over the world which is based on image processing service. Earlier CAD tools were designed using conventional machine learning techniue using semi-automatic approach. The modern growth of deep learning for automatic detection and classification leads to significant improvement in accuracy. This paper the automatic CAD framework for liver ca

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Teramoto, Atsushi, Tetsuya Tsukamoto, Yuka Kiriyama, and Hiroshi Fujita. "Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks." BioMed Research International 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/4067832.

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Lung cancer is a leading cause of death worldwide. Currently, in differential diagnosis of lung cancer, accurate classification of cancer types (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma) is required. However, improving the accuracy and stability of diagnosis is challenging. In this study, we developed an automated classification scheme for lung cancers presented in microscopic images using a deep convolutional neural network (DCNN), which is a major deep learning technique. The DCNN used for classification consists of three convolutional layers, three pooling layers, a

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Merikapudi, Dr Seshaiah, Prof Rame Gowda M, Dr Shwetha V, and Dr Harshvardhan Doddamane. "Automated Detection of Breast Lump/ Masses through Mammogram Image Analysis." June-July 2023, no. 34 (July 30, 2023): 36–43. http://dx.doi.org/10.55529/jipirs.34.36.43.

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Abstract (sommario):

Current technology is playing a key role in the field of health care. As everything is getting automated here is an attempt to automate the identification of lumps in the breast. Lump in the breast can be a sign of breast cancer. The uncontrolled growth of breast cells is the reason for lumps or cancer in the breast. Not all lumps in the breasts are cancer. Still early identification and staging of the disease is critical in planning the treatment of breast cancer. In late stages malignancy can extend beyond the breast and spread to surrounding structures. Mammogram is the gold standard proced

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AHAMED J, NOOR. "Breast Cancer Detection using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42004.

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Abstract (sommario):

One of the most researched issues in the medical field is the diagnosis of breast cancer. Numerous studies have been conducted on cancer diagnosis, highlighting the necessity of early cancer illness prediction. Health records are used as input to an automated system in order to provide advance prediction. The main goal of the paper is to build an automated system using recurrent neural network models based on deep learning. This research proposes an LSTM-BRNN-GRU that uses a patient's medical records to assess the likelihood that the patient will get breast cancer. The suggested model is contr

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Priyadarshani Behera, Mandakini, Archana Sarangi, and Debahuti Mishra. "Enhancing Breast Cancer Diagnosis." International journal of electrical and computer engineering systems 15, no. 6 (2024): 523–30. http://dx.doi.org/10.32985/ijeces.15.6.6.

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Abstract (sommario):

Breast cancer stands as a significant global health challenge, ranking as the second leading cause of mortality among women. The increasing complexity of timely and accurate remote diagnosis has spurred the need for advanced technological solutions. Breast cancer prediction involves utilizing risk assessment models to identify individuals at higher risk, enabling early detection and personalized treatment strategies. This research meticulously assesses the effectiveness of various long short-term memory (LSTM) classifiers, including simple LSTM, Vanilla LSTM, Stacked LSTM, and Bidirectional LS

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Anand, Vatsala, Sheifali Gupta, Ayman Altameem, Soumya Ranjan Nayak, Ramesh Chandra Poonia, and Abdul Khader Jilani Saudagar. "An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer." Diagnostics 12, no. 7 (2022): 1628. http://dx.doi.org/10.3390/diagnostics12071628.

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Abstract (sommario):

Skin cancer is the most commonly diagnosed and reported malignancy worldwide. To reduce the death rate from cancer, it is essential to diagnose skin cancer at a benign stage as soon as possible. To save lives, an automated system that can detect skin cancer in its earliest stages is necessary. For the diagnosis of skin cancer, various researchers have performed tasks using deep learning and transfer learning models. However, the existing literature is limited in terms of its accuracy and its troublesome and time-consuming process. As a result, it is critical to design an automatic system that

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T. Chalapathi, Rao, and Naik Kshiramani. "Breast cancer disease prediction using ensemble techniques." i-manager’s Journal on Image Processing 10, no. 1 (2023): 7. http://dx.doi.org/10.26634/jip.10.1.19238.

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Abstract (sommario):

Breast Cancer is a highly lethal reproductive cancer that disproportionately affects women and is a leading cause of death worldwide. Cancer is characterized by the uncontrolled division and invasion of abnormal cells into the surrounding tissues. Early detection is crucial in the diagnosis of Breast Cancer, as it accounts for a significant percentage of cancer diagnoses and deaths among women. To prevent unnecessary tests, accurate classification of malignant and benign tumors is necessary. Researchers have developed numerous automated classification methods for Breast Cancer, with soft compu

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Rida Ali, Adeel Shahzad, Mohsin Ali Tariq, Muhammad Fuzail, Ahmad Naeem, and Naeem Aslam. "AUTOMATED LUNG CANCER RECOGNITION BY A CONVOLUTIONAL NEURAL NETWORK." Kashf Journal of Multidisciplinary Research 2, no. 06 (2025): 275–92. https://doi.org/10.71146/kjmr511.

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Abstract (sommario):

Cancer of the lung is a massively common fatal kind of cancer in not only medical practice but also the world. It is the second common cancer diagnosed and causes more than 71 % of deaths as a result of non-communicable diseases (NCDs). It is also important that early and accurate diagnosis should be made in order to increase the survival rates of patients and their desired outcome. One of the most valid methods of establishing a certain type and stage of lung cancer is to carry out a histopathological examination of the lung tissue. Manual examination of histopathological slides, however, con

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Dutta, Shawni, Jyotsna Kumar Mandal, Tai Hoon Kim, and Samir Kumar Bandyopadhyay. "Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN." Applied Computer Systems 25, no. 2 (2020): 163–71. http://dx.doi.org/10.2478/acss-2020-0018.

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Abstract (sommario):

Abstract Breast Cancer diagnosis is one of the most studied problems in the medical domain. Cancer diagnosis has been studied extensively, which instantiates the need for early prediction of cancer disease. To obtain advance prediction, health records are exploited and given as input to an automated system. The paper focuses on constructing an automated system by employing deep learning based recurrent neural network models. A stacked GRU-LSTM-BRNN is proposed in this paper that accepts health records of a patient for determining the possibility of being affected by breast cancer. The proposed

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Takahashi, Yu, Kenbun Sone, Katsuhiko Noda, et al. "Automated system for diagnosing endometrial cancer by adopting deep-learning technology in hysteroscopy." PLOS ONE 16, no. 3 (2021): e0248526. http://dx.doi.org/10.1371/journal.pone.0248526.

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Abstract (sommario):

Endometrial cancer is a ubiquitous gynecological disease with increasing global incidence. Therefore, despite the lack of an established screening technique to date, early diagnosis of endometrial cancer assumes critical importance. This paper presents an artificial-intelligence-based system to detect the regions affected by endometrial cancer automatically from hysteroscopic images. In this study, 177 patients (60 with normal endometrium, 21 with uterine myoma, 60 with endometrial polyp, 15 with atypical endometrial hyperplasia, and 21 with endometrial cancer) with a history of hysteroscopy w

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