Deep Learning Based Pox Disease Detection and Generation of Synthesis Data with GAN Model

Nilgün Şengöz; Emine Vargün; Harun Köroğlu

doi:10.47000/tjmcs.1725690

Deep Learning Based Pox Disease Detection and Generation of Synthesis Data with GAN Model

Abstract

This study focuses on the classification of smallpox histopathological images using deep learning-based approaches. Given the challenges posed by limited dataset availability in medical image analysis, multiple techniques were employed to improve classification performance. Initially, a baseline classification was performed using a small dataset consisting of 600 images, which yielded a moderate accuracy of 75$\%$. To enhance the model's generalization capability, data augmentation techniques were applied, expanding the dataset by four times and increasing the classification accuracy to 96.23$\%$. Additionally, Generative Adversarial Networks (GAN) were used to generate synthetic data, further augmenting the dataset. The classification model trained on GAN-generated synthetic data achieved a high accuracy of 98.33$\%$ after proper hyperparameter tuning, demonstrating the efficacy of using synthetic data to improve performance. This study highlights the importance of data augmentation and synthetic data generation in medical image classification tasks, especially when working with limited datasets. The results suggest that a well-tuned deep learning model, combined with advanced data generation techniques, can provide accurate and reliable classification outcomes, potentially contributing to more effective diagnostic processes in clinical applications.

Keywords

References

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Details

Primary Language

English

Subjects

Machine Vision

Journal Section

Research Article

Authors

Nilgün Şengöz ^*
0000-0001-5651-8173
Türkiye

Emine Vargün
0009-0009-6913-2996
Türkiye

Harun Köroğlu
0009-0007-5371-4153
Türkiye

Publication Date

June 30, 2026

Submission Date

June 23, 2025

Acceptance Date

February 18, 2026

Published in Issue

Year 2026 Volume: 18 Number: 2

DOI

https://doi.org/10.47000/tjmcs.1725690

IZ

https://izlik.org/JA48AY93GM

Cite

RIS / Bibtex

APA

Şengöz, N., Vargün, E., & Köroğlu, H. (2026). Deep Learning Based Pox Disease Detection and Generation of Synthesis Data with GAN Model. Turkish Journal of Mathematics and Computer Science, 18(2), 541-551. https://doi.org/10.47000/tjmcs.1725690

AMA

1.Şengöz N, Vargün E, Köroğlu H. Deep Learning Based Pox Disease Detection and Generation of Synthesis Data with GAN Model. TJMCS. 2026;18(2):541-551. doi:10.47000/tjmcs.1725690

Chicago

Şengöz, Nilgün, Emine Vargün, and Harun Köroğlu. 2026. “Deep Learning Based Pox Disease Detection and Generation of Synthesis Data With GAN Model”. Turkish Journal of Mathematics and Computer Science 18 (2): 541-51. https://doi.org/10.47000/tjmcs.1725690.

EndNote

Şengöz N, Vargün E, Köroğlu H (June 1, 2026) Deep Learning Based Pox Disease Detection and Generation of Synthesis Data with GAN Model. Turkish Journal of Mathematics and Computer Science 18 2 541–551.

IEEE

[1]N. Şengöz, E. Vargün, and H. Köroğlu, “Deep Learning Based Pox Disease Detection and Generation of Synthesis Data with GAN Model”, TJMCS, vol. 18, no. 2, pp. 541–551, June 2026, doi: 10.47000/tjmcs.1725690.

ISNAD

Şengöz, Nilgün - Vargün, Emine - Köroğlu, Harun. “Deep Learning Based Pox Disease Detection and Generation of Synthesis Data With GAN Model”. Turkish Journal of Mathematics and Computer Science 18/2 (June 1, 2026): 541-551. https://doi.org/10.47000/tjmcs.1725690.

JAMA

1.Şengöz N, Vargün E, Köroğlu H. Deep Learning Based Pox Disease Detection and Generation of Synthesis Data with GAN Model. TJMCS. 2026;18:541–551.

MLA

Şengöz, Nilgün, et al. “Deep Learning Based Pox Disease Detection and Generation of Synthesis Data With GAN Model”. Turkish Journal of Mathematics and Computer Science, vol. 18, no. 2, June 2026, pp. 541-5, doi:10.47000/tjmcs.1725690.

Vancouver

1.Nilgün Şengöz, Emine Vargün, Harun Köroğlu. Deep Learning Based Pox Disease Detection and Generation of Synthesis Data with GAN Model. TJMCS. 2026 Jun. 1;18(2):541-5. doi:10.47000/tjmcs.1725690