{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T07:33:06Z","timestamp":1768807986903,"version":"3.49.0"},"reference-count":26,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T00:00:00Z","timestamp":1673568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper describes the process of developing a classification model for the effective detection of malignant melanoma, an aggressive type of cancer in skin lesions. Primary focus is given on fine-tuning and improving a state-of-the-art convolutional neural network (CNN) to obtain the optimal ROC-AUC score. The study investigates a variety of artificial intelligence (AI) clustering techniques to train the developed models on a combined dataset of images across data from the 2019 and 2020 IIM-ISIC Melanoma Classification Challenges. The models were evaluated using varying cross-fold validations, with the highest ROC-AUC reaching a score of 99.48%.<\/jats:p>","DOI":"10.3390\/s23020926","type":"journal-article","created":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T02:57:33Z","timestamp":1673578653000},"page":"926","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Intraclass Clustering-Based CNN Approach for Detection of Malignant Melanoma"],"prefix":"10.3390","volume":"23","author":[{"given":"Adrian D.","family":"Bandy","sequence":"first","affiliation":[{"name":"Department of Networks and Digital Media, Kingston University, London KT1 1LQ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2028-0367","authenticated-orcid":false,"given":"Yannis","family":"Spyridis","sequence":"additional","affiliation":[{"name":"Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield S1 3JD, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2846-0610","authenticated-orcid":false,"given":"Barbara","family":"Villarini","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Westminster, London W1B 2HW, UK"}]},{"given":"Vasileios","family":"Argyriou","sequence":"additional","affiliation":[{"name":"Department of Networks and Digital Media, Kingston University, London KT1 1LQ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cazzato, G., Colagrande, A., Ingravallo, G., Lettini, T., Filoni, A., Ambrogio, F., Bonamonte, D., Dellino, M., Lupo, C., and Casatta, N. 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