{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T14:00:47Z","timestamp":1779199247207,"version":"3.51.4"},"reference-count":26,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T00:00:00Z","timestamp":1768262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Melanoma is highly dangerous and can spread rapidly to other parts of the body. It has an increasing fatality rate among different types of cancer. Timely detection of skin malignancies can reduce overall mortality. Therefore, clinical screening methods require more time and accuracy for diagnosis. An automated, computer-aided system would facilitate earlier melanoma detection, thereby increasing patient survival rates. This paper identifies melanoma images using a Convolutional Neural Network. Skin images are preprocessed using Histogram Equalization and Gabor transforms. A Gabor filter-based Convolutional Neural Network (CNN) classifier trains and classifies the extracted features. We adopt Gabor filters because they are bandpass filters that transform a pixel into a multi-resolution kernel matrix, providing detailed information about the image. This study suggests a method with accuracy, sensitivity, and specificity of 98.58%, 98.66%, and 98.75%, respectively. This research supports SDGs 3 and 4 by facilitating early melanoma detection and enhancing AI-driven medical education.<\/jats:p>","DOI":"10.3390\/computers15010054","type":"journal-article","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T18:40:09Z","timestamp":1768329609000},"page":"54","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Gabor Transform-Based Deep Learning System Using CNN for Melanoma Detection"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7174-0784","authenticated-orcid":false,"given":"S.","family":"Deivasigamani","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Technology & Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3775-5621","authenticated-orcid":false,"given":"C.","family":"Senthilpari","sequence":"additional","affiliation":[{"name":"Faculty of Artificial Intelligence and Engineering, Multimedia University, Cyberjaya Campus, Cyberjaya 63100, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siva Sundhara Raja.","family":"D","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, SACS MAVMM Engineering College, Madurai 625301, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.","family":"Thankaraj","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Rrase College of Engineering, Chennai 603103, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G.","family":"Narmadha","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Sethu Institute of Technology, Virudhunagar 626106, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.","family":"Gowrishankar","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, AMET University, Chennai 603112, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"30542","DOI":"10.1038\/s41598-024-81961-3","article-title":"Decoding skin cancer classification: Perspectives, insights, and advances through researchers\u2019 lens","volume":"14","author":"Ray","year":"2024","journal-title":"Sci. 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