Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images

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dc.contributor.authorGüler, Recep
dc.contributor.authorKarapinar Şentürk, Zehra
dc.contributor.authorGamsizkan, Mehmet
dc.contributor.authorOzcan, Yunus
dc.date.accessioned2025-10-11T20:45:31Z
dc.date.available2025-10-11T20:45:31Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractSkin diseases are very common all over the world. The examination can be done by photographing the relevant area or taking a tissue sample to diagnose skin diseases. Examining tissue samples allows examination at the cellular level. This study discussed three skin diseases: lichen sclerosus, morphea, and cutaneous small vessel vasculitis (vasculitis). For this problem, which does not have an open-access dataset in the literature, a dataset consisting of histopathological images belonging to each class was created. Convolutional neural network models were created for this three-class classification problem, and their results were evaluated. In addition, in this problem where it is difficult to obtain sample images, the efficiency of transfer learning methods was evaluated with a limited number of examples. For this purpose, tests were performed with VGG16, ResNet50, InceptionV3, and EfficientNetB4 models, and the results were given. Among all the results, the accuracy value of the VGG16 model was 0.9755 and gave the best result. However, although the accuracy value was quite good, precision, recall, and f1-score metrics values were around 0.65. This shows deficiencies in how often the model correctly predicts the positive class and how well it predicts all positive examples in the dataset. © 2025 Elsevier B.V., All rights reserved.en_US
dc.identifier.doi10.35377/saucis...1582098
dc.identifier.endpage321en_US
dc.identifier.issn2636-8129
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-105010145821en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage312en_US
dc.identifier.urihttps://doi.org/10.35377/saucis...1582098
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21384
dc.identifier.volume8en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSakarya Universityen_US
dc.relation.ispartofSakarya University Journal of Computer and Information Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_Scopus_20250911
dc.subjectConvolutional Neural Networksen_US
dc.subjectData Augmentationen_US
dc.subjectHistopathologyen_US
dc.subjectTransfer Learningen_US
dc.titleDiagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Imagesen_US
dc.typeArticleen_US

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