Artificial Intelligence-Based Screening for Diabetic Retinopathy: Model Comparison and Interpretability

Muhammed Telçeken; Şeyma Değirmenci

doi:10.35377/saucis.8.94717.1754835

EN

Artificial Intelligence-Based Screening for Diabetic Retinopathy: Model Comparison and Interpretability

Abstract

Diabetic Retinopathy is one of the common complications of diabetes and can lead to permanent vision loss if left untreated. This study examined the performance of different AI-based methods for DR classification. Deep learning-based models, ResNet-50, DenseNet-121, U-Net, and classical CNN structures, along with traditional machine learning algorithms, SVM, Decision Trees, and k-Nearest Neighbor, were evaluated on the APTOS 2019 dataset. To optimize model performance, image data were subjected to various preprocessing steps, such as resizing, contrast correction, and denoising. Augmentation techniques were used to increase data diversity. According to experimental results, the most successful model was DenseNet-121, with an accuracy rate of 87% and an F1 score of 86%. In contrast, while classical machine learning methods produce lower accuracy values than deep learning, they exhibit consistent performance under certain conditions and offer a more computationally cost-effective alternative. The comparisons indicate the applicability of classical methods, especially in scenarios with limited data. This evaluation process creates a basic framework that will enable the integration of explainable artificial intelligence (XAI) approaches in later stages and is a preparation for adapting interpretation techniques such as SHAP and LIME to clinical decision support systems.

Keywords

References

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Details

Primary Language

English

Subjects

Computer Software , Software Testing, Verification and Validation

Journal Section

Research Article

Authors

Muhammed Telçeken ^*
0000-0001-5223-2856
Türkiye

Şeyma Değirmenci
0009-0000-1501-5086
Türkiye

Early Pub Date

September 29, 2025

Publication Date

September 30, 2025

Submission Date

July 31, 2025

Acceptance Date

August 29, 2025

Published in Issue

Year 2025 Volume: 8 Number: 3

DOI

https://doi.org/10.35377/saucis.8.94717.1754835

IZ

https://izlik.org/JA73MK55PC

Cite

RIS / Bibtex

APA

Telçeken, M., & Değirmenci, Ş. (2025). Artificial Intelligence-Based Screening for Diabetic Retinopathy: Model Comparison and Interpretability. Sakarya University Journal of Computer and Information Sciences, 8(3), 510-517. https://doi.org/10.35377/saucis.8.94717.1754835

AMA

1.Telçeken M, Değirmenci Ş. Artificial Intelligence-Based Screening for Diabetic Retinopathy: Model Comparison and Interpretability. SAUCIS. 2025;8(3):510-517. doi:10.35377/saucis.8.94717.1754835

Chicago

Telçeken, Muhammed, and Şeyma Değirmenci. 2025. “Artificial Intelligence-Based Screening for Diabetic Retinopathy: Model Comparison and Interpretability”. Sakarya University Journal of Computer and Information Sciences 8 (3): 510-17. https://doi.org/10.35377/saucis.8.94717.1754835.

EndNote

Telçeken M, Değirmenci Ş (September 1, 2025) Artificial Intelligence-Based Screening for Diabetic Retinopathy: Model Comparison and Interpretability. Sakarya University Journal of Computer and Information Sciences 8 3 510–517.

IEEE

[1]M. Telçeken and Ş. Değirmenci, “Artificial Intelligence-Based Screening for Diabetic Retinopathy: Model Comparison and Interpretability”, SAUCIS, vol. 8, no. 3, pp. 510–517, Sept. 2025, doi: 10.35377/saucis.8.94717.1754835.

ISNAD

Telçeken, Muhammed - Değirmenci, Şeyma. “Artificial Intelligence-Based Screening for Diabetic Retinopathy: Model Comparison and Interpretability”. Sakarya University Journal of Computer and Information Sciences 8/3 (September 1, 2025): 510-517. https://doi.org/10.35377/saucis.8.94717.1754835.

JAMA

1.Telçeken M, Değirmenci Ş. Artificial Intelligence-Based Screening for Diabetic Retinopathy: Model Comparison and Interpretability. SAUCIS. 2025;8:510–517.

MLA

Telçeken, Muhammed, and Şeyma Değirmenci. “Artificial Intelligence-Based Screening for Diabetic Retinopathy: Model Comparison and Interpretability”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 3, Sept. 2025, pp. 510-7, doi:10.35377/saucis.8.94717.1754835.

Vancouver

1.Muhammed Telçeken, Şeyma Değirmenci. Artificial Intelligence-Based Screening for Diabetic Retinopathy: Model Comparison and Interpretability. SAUCIS. 2025 Sep. 1;8(3):510-7. doi:10.35377/saucis.8.94717.1754835