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

Recep Güler; Zehra Karapınar Şentürk; Mehmet Gamsızkan; Yunus Özcan

doi:10.35377/saucis...1582098

EN

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

Abstract

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.

Keywords

Ethical Statement

This study does not require permission from the ethics committee or any special permission. The microscopic skin images used do not contain any personal data.

Thanks

The authors would like to thank Research Assistant Dr. Sümeyye Güneş Takır for creating the dataset used in this study.

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Details

Primary Language

English

Subjects

Computer Software

Journal Section

Research Article

Authors

Recep Güler
0009-0004-9715-9006
Türkiye

Zehra Karapınar Şentürk ^*
0000-0003-3116-1985
Türkiye

Mehmet Gamsızkan
0000-0001-9942-4898
Türkiye

Yunus Özcan
0000-0002-2295-1152
Türkiye

Early Pub Date

June 20, 2025

Publication Date

June 30, 2025

Submission Date

November 10, 2024

Acceptance Date

April 12, 2025

Published in Issue

Year 2025 Volume: 8 Number: 2

DOI

https://doi.org/10.35377/saucis...1582098

IZ

https://izlik.org/JA55YG23BA

Cite

RIS / Bibtex

APA

Güler, R., Karapınar Şentürk, Z., Gamsızkan, M., & Özcan, Y. (2025). Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images. Sakarya University Journal of Computer and Information Sciences, 8(2), 312-321. https://doi.org/10.35377/saucis...1582098

AMA

1.Güler R, Karapınar Şentürk Z, Gamsızkan M, Özcan Y. Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images. SAUCIS. 2025;8(2):312-321. doi:10.35377/saucis.1582098

Chicago

Güler, Recep, Zehra Karapınar Şentürk, Mehmet Gamsızkan, and Yunus Özcan. 2025. “Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images”. Sakarya University Journal of Computer and Information Sciences 8 (2): 312-21. https://doi.org/10.35377/saucis. 1582098.

EndNote

Güler R, Karapınar Şentürk Z, Gamsızkan M, Özcan Y (June 1, 2025) Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images. Sakarya University Journal of Computer and Information Sciences 8 2 312–321.

IEEE

[1]R. Güler, Z. Karapınar Şentürk, M. Gamsızkan, and Y. Özcan, “Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images”, SAUCIS, vol. 8, no. 2, pp. 312–321, June 2025, doi: 10.35377/saucis...1582098.

ISNAD

Güler, Recep - Karapınar Şentürk, Zehra - Gamsızkan, Mehmet - Özcan, Yunus. “Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images”. Sakarya University Journal of Computer and Information Sciences 8/2 (June 1, 2025): 312-321. https://doi.org/10.35377/saucis. 1582098.

JAMA

1.Güler R, Karapınar Şentürk Z, Gamsızkan M, Özcan Y. Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images. SAUCIS. 2025;8:312–321.

MLA

Güler, Recep, et al. “Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 2, June 2025, pp. 312-21, doi:10.35377/saucis. 1582098.

Vancouver

1.Recep Güler, Zehra Karapınar Şentürk, Mehmet Gamsızkan, Yunus Özcan. Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images. SAUCIS. 2025 Jun. 1;8(2):312-21. doi:10.35377/saucis. 1582098