saucis

Sakarya University Journal of Computer and Information Sciences

2636-8129

Sakarya University

10.35377/saucis...1582098

Computer Software

Bilgisayar Yazılımı

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

https://orcid.org/0009-0004-9715-9006

Güler

Recep

DÜZCE ÜNİVERSİTESİ, MESLEK YÜKSEKOKULU

https://orcid.org/0000-0003-3116-1985

Karapınar Şentürk

Zehra

DÜZCE ÜNİVERSİTESİ

https://orcid.org/0000-0001-9942-4898

Gamsızkan

Mehmet

DÜZCE ÜNİVERSİTESİ, TIP FAKÜLTESİ

https://orcid.org/0000-0002-2295-1152

Özcan

Yunus

DÜZCE ÜNİVERSİTESİ, TIP FAKÜLTESİ

06 30 2025

8 2 312 321 11 10 2024 04 12 2025

2018

Sakarya University Journal of Computer and Information Sciences

Skin 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.

Convolutional neural networks Data augmentation Transfer learning Histopathology

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