Automatic Diagnosis of Skin Diseases with Convolutional Neural Networks on Multi-Class Visual Data

Hilal Biyik; Duygu Kaya; Ayhan Akbal

doi:10.17678/beuscitech.1754394

Automatic Diagnosis of Skin Diseases with Convolutional Neural Networks on Multi-Class Visual Data

Abstract

The automatic diagnosis of skin diseases is of great importance, especially in cases requiring early detection, as it accelerates clinical processes and reduces the margin of error. In this study, a classification model based on Convolutional Neural Network (CNN) architectures was developed on a multi-class visual dataset containing three different skin disease categories. To enhance the model’s performance, data augmentation techniques were applied, and the images were resized to 224×224 pixels. Using a transfer learning approach, the model was trained with preprocessing ResNet-18, AlexNet, and DenseNet-201 architectures. The hyperparameters used during the training process were carefully selected, and the model's training and validation accuracies were monitored. According to the results obtained, the ResNet-18 model demonstrated strong performance with an accuracy of 87.19% on the test set. These findings indicate that deep learning-based architectures can be effectively applied in the multi-class diagnosis of skin diseases.

Keywords

Ethical Statement

The study is complied with research and publication ethics.

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Details

Primary Language

English

Subjects

Deep Learning

Journal Section

Research Article

Authors

Hilal Biyik ^*
0009-0007-1816-6868
Türkiye

Duygu Kaya
0000-0002-6453-631X
Türkiye

Ayhan Akbal
0000-0003-4820-3563
Türkiye

Publication Date

December 31, 2025

Submission Date

July 31, 2025

Acceptance Date

November 19, 2025

Published in Issue

Year 2025 Volume: 15 Number: 2

DOI

https://doi.org/10.17678/beuscitech.1754394

IZ

https://izlik.org/JA65RM33AX

Cite

RIS / Bibtex

APA

Biyik, H., Kaya, D., & Akbal, A. (2025). Automatic Diagnosis of Skin Diseases with Convolutional Neural Networks on Multi-Class Visual Data. Bitlis Eren University Journal of Science and Technology, 15(2), 171-194. https://doi.org/10.17678/beuscitech.1754394

AMA

1.Biyik H, Kaya D, Akbal A. Automatic Diagnosis of Skin Diseases with Convolutional Neural Networks on Multi-Class Visual Data. Bitlis Eren University Journal of Science and Technology. 2025;15(2):171-194. doi:10.17678/beuscitech.1754394

Chicago

Biyik, Hilal, Duygu Kaya, and Ayhan Akbal. 2025. “Automatic Diagnosis of Skin Diseases With Convolutional Neural Networks on Multi-Class Visual Data”. Bitlis Eren University Journal of Science and Technology 15 (2): 171-94. https://doi.org/10.17678/beuscitech.1754394.

EndNote

Biyik H, Kaya D, Akbal A (December 1, 2025) Automatic Diagnosis of Skin Diseases with Convolutional Neural Networks on Multi-Class Visual Data. Bitlis Eren University Journal of Science and Technology 15 2 171–194.

IEEE

[1]H. Biyik, D. Kaya, and A. Akbal, “Automatic Diagnosis of Skin Diseases with Convolutional Neural Networks on Multi-Class Visual Data”, Bitlis Eren University Journal of Science and Technology, vol. 15, no. 2, pp. 171–194, Dec. 2025, doi: 10.17678/beuscitech.1754394.

ISNAD

Biyik, Hilal - Kaya, Duygu - Akbal, Ayhan. “Automatic Diagnosis of Skin Diseases With Convolutional Neural Networks on Multi-Class Visual Data”. Bitlis Eren University Journal of Science and Technology 15/2 (December 1, 2025): 171-194. https://doi.org/10.17678/beuscitech.1754394.

JAMA

1.Biyik H, Kaya D, Akbal A. Automatic Diagnosis of Skin Diseases with Convolutional Neural Networks on Multi-Class Visual Data. Bitlis Eren University Journal of Science and Technology. 2025;15:171–194.

MLA

Biyik, Hilal, et al. “Automatic Diagnosis of Skin Diseases With Convolutional Neural Networks on Multi-Class Visual Data”. Bitlis Eren University Journal of Science and Technology, vol. 15, no. 2, Dec. 2025, pp. 171-94, doi:10.17678/beuscitech.1754394.

Vancouver

1.Hilal Biyik, Duygu Kaya, Ayhan Akbal. Automatic Diagnosis of Skin Diseases with Convolutional Neural Networks on Multi-Class Visual Data. Bitlis Eren University Journal of Science and Technology. 2025 Dec. 1;15(2):171-94. doi:10.17678/beuscitech.1754394