A Comparative Study of Various Transfer Learning Models on Skin Cancer Confirmation Methods

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dc.contributor.authorMehmet Ali Altuncu
dc.contributor.authorKaplan Kaplan
dc.contributor.authorMelih Kuncan
dc.date.accessioned2025-03-19T07:56:54Z
dc.date.available2025-03-19T07:56:54Z
dc.date.issued2025-02-28
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü
dc.description.abstractSkin cancer confirmation is critical in determining a patient's treatment planning process after diagnosis. A proper confirmation process enables the determination of the type, stage, and other characteristics of skin cancer, helping to plan the appropriate treatment. These methods prevent the progression of the disease, thereby contributing to a better response to treatment and improving the patient's quality of life. Dermoscopic images are commonly used to confirm skin cancer types. To obtain meaningful results from these images, researchers often apply artificial intelligence techniques in such studies. Specifically, transfer learning models have been commonly used to enhance the features of these images due to the limited availability of medical image data and the difficulty in extracting meaningful information from such data. While most studies focus on classifying skin cancer types, this research aims to classify skin cancer were used for this purpose. The dataset includes four different confirmation methods: confocal, consensus, follow-up, and histopathology. Four distinct transfer learning models-Resnet-50, Resnet-101, VGG19, and InceptionResnetV2-were utilized. Additionally, ensemble learning was conducted based on the results of these models using the maximum voting approach. The highest success rate was achieved with Resnet-101 at 96.04%. Considering the comparative results, the accuracy of our promising model proved to be significantly high.
dc.identifier.citationAltuncu, M. A., Kaplan, K., & Kuncan, M. (2025). A Comparative Study of Various Transfer Learning Models on Skin Cancer Confirmation Methods. Journal of Universal Computer Science, 31(2), 113.
dc.identifier.doi10.3897/jucs.118220
dc.identifier.issn0948-6968
dc.identifier.issn0948-695X
dc.identifier.issue2
dc.identifier.urihttps://doi.org/10.3897/jucs.118220
dc.identifier.urihttps://hdl.handle.net/20.500.12604/8573
dc.identifier.volume31
dc.identifier.wosWOS:001438798800002
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.institutionauthorKuncan, Melih
dc.institutionauthorid0000-0002-9749-0418
dc.language.isoen
dc.publisherPensoft Publishers
dc.relation.ispartofJUCS - Journal of Universal Computer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectClassification
dc.subjectskin cancer
dc.subjecttransfer learning
dc.subjectdiagnosis
dc.subjectdeep learning
dc.titleA Comparative Study of Various Transfer Learning Models on Skin Cancer Confirmation Methods
dc.typejournal-article
oaire.citation.issue2
oaire.citation.volume31

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