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

Year 2025, Volume: 8 Issue: 3, 510 - 517, 30.09.2025

Muhammed Telçeken , Şeyma Değirmenci

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

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

Diabetic Retinopathy CNN K-NN SVM Decision Trees

References

  • Yau, J. W., Rogers, S. L., Kawasaki, R., Lamoureux, E. L., Kowalski, J. W., Bek, T., ... & Meta-Analysis for Eye Disease (META-EYE) Study Group. (2012). Global prevalence and major risk factors of diabetic retinopathy. Diabetes care, 35(3), 556-564.
  • Seo, H., Park, S.-J., & Song, M. (2025). Diabetic Retinopathy (DR): Mechanisms, Current Therapies, and Emerging Strategies. Cells, 14(5), 376.
  • Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detecting diabetic retinopathy in retinal fundus photographs. jama, 316(22), 2402-2410.
  • Ting, D. S. W., Cheung, C. Y. L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., ... & Wong, T. Y. (2017). Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. Jama, 318(22), 2211-2223.
  • Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., & Zheng, Y. (2016). Convolutional neural networks for diabetic retinopathy. Procedia computer science, 90, 200-205.
  • Sebastian, A., Elharrouss, O., Al-Maadeed, S., & Almaadeed, N. (2023). A survey on deep-learning-based diabetic retinopathy classification. Diagnostics, 13(3), 345.
  • Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature machine intelligence, 1(5), 206-215.
  • Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). " Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144).
  • Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.
  • Atila, Ü., Akyol, K., & Sabaz, F. (2020). Retinal Görüntülerde Eksuda Lezyonlarının Tespiti Üzerine Bir Çalışma. Bilişim Teknolojileri Dergisi, 13(1), 27-36.
  • Tanyıldızı, E., & Okur, S. (2016). Retina Görüntülerindeki Kan Damarlarının Belirlenmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 28(2), 15-22.
  • Şehirli, E. (2014). Diyabetik hastalarda retinal damar paketinin bilgisayarlı görü yöntemleri ile görüntülenmesi ve olası mikroanevrizma lezyonlarının ortaya koyulması (Master's thesis, Fen Bilimleri Enstitüsü).
  • Çakar, O. (2019). Retina görüntülerinde koroid kalınlıklarının görüntü işleme teknikleri kullanılarak ölçülmesi (Master's thesis, Konya Teknik Üniversitesi).
  • Özçelik, Y. B., & Altan, A. (2021). Diyabetik retinopati teşhisi için fundus görüntülerinin derin öğrenme tabanlı sınıflandırılması. Avrupa Bilim ve Teknoloji Dergisi, (29), 156-167.
  • Yalçin, N., Alver, S., & Uluhatun, N. (2018, May). Classification of retinal images with deep learning for early detection of diabetic retinopathy disease. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  • Budak, D. (2024). Diyabetik retinopati teşhisi için derin öğrenme tabanlı sınıflandırma analizi (Master's thesis, Aksaray Üniversitesi Sosyal Bilimler Enstitüsü).
  • Ağca, K., & Takcı, H. (2022). Hibrit Bir Model Oluşturarak Diyabetik Retinopati Sınıflandırılması. Avrupa Bilim ve Teknoloji Dergisi, (36), 227-236.
  • Ciran, A., & Özbay, E. (2024). Derin Öğrenme ve Özellik Seçimi Yaklaşımları Kullanılarak Göz Hastalıkları Tespiti. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 15(2), 421-433.
  • Ramaha, N., & Imad, S. (2023). Derin Öğrenmeye Karşı Makine Kullanarak Diyabetik Retinopati Teşhisi. Avrupa Bilim ve Teknoloji Dergisi, (51), 301-313.
  • Çavlı, A., & Toğaçar, M. (2023). Yapay Zekâ Yaklaşımlarını Kullanarak Retinopati Hastalığının Tespiti. Mühendislik Bilimleri ve Araştırmaları Dergisi, 5(1), 88-97.
  • Polater, S. N., & Isık, A. H. (2024). Diyabetik Retinopati Tespiti İçin Derin Öğrenmeye Dayalı Sınıflandırma. Uluborlu Mesleki Bilimler Dergisi, 7(2), 13-19.
  • APTOS 2019 blindness detection, 2019, [online] Available: https://www.kaggle.com/c/aptos2019-blindness-detection/data.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
  • Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).
  • Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 (pp. 234-241). Springer international publishing.
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20, 273-297.
  • Schölkopf, B., & Smola, A. J. (2002). Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press.
  • Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1, 81-106.
  • Rokach, L., & Maimon, O. (2005). Decision trees. Data mining and knowledge discovery handbook, 165-192.
  • Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1), 21-27.
  • Telceken, M. (2025). A new feature extraction method for AI based classification of heart sounds: dual-frequency cepstral coefficients (DFCCs). The European Physical Journal Special Topics, 1-12.

Year 2025, Volume: 8 Issue: 3, 510 - 517, 30.09.2025

Muhammed Telçeken , Şeyma Değirmenci

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

Abstract

References

  • Yau, J. W., Rogers, S. L., Kawasaki, R., Lamoureux, E. L., Kowalski, J. W., Bek, T., ... & Meta-Analysis for Eye Disease (META-EYE) Study Group. (2012). Global prevalence and major risk factors of diabetic retinopathy. Diabetes care, 35(3), 556-564.
  • Seo, H., Park, S.-J., & Song, M. (2025). Diabetic Retinopathy (DR): Mechanisms, Current Therapies, and Emerging Strategies. Cells, 14(5), 376.
  • Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detecting diabetic retinopathy in retinal fundus photographs. jama, 316(22), 2402-2410.
  • Ting, D. S. W., Cheung, C. Y. L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., ... & Wong, T. Y. (2017). Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. Jama, 318(22), 2211-2223.
  • Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., & Zheng, Y. (2016). Convolutional neural networks for diabetic retinopathy. Procedia computer science, 90, 200-205.
  • Sebastian, A., Elharrouss, O., Al-Maadeed, S., & Almaadeed, N. (2023). A survey on deep-learning-based diabetic retinopathy classification. Diagnostics, 13(3), 345.
  • Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature machine intelligence, 1(5), 206-215.
  • Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). " Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144).
  • Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.
  • Atila, Ü., Akyol, K., & Sabaz, F. (2020). Retinal Görüntülerde Eksuda Lezyonlarının Tespiti Üzerine Bir Çalışma. Bilişim Teknolojileri Dergisi, 13(1), 27-36.
  • Tanyıldızı, E., & Okur, S. (2016). Retina Görüntülerindeki Kan Damarlarının Belirlenmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 28(2), 15-22.
  • Şehirli, E. (2014). Diyabetik hastalarda retinal damar paketinin bilgisayarlı görü yöntemleri ile görüntülenmesi ve olası mikroanevrizma lezyonlarının ortaya koyulması (Master's thesis, Fen Bilimleri Enstitüsü).
  • Çakar, O. (2019). Retina görüntülerinde koroid kalınlıklarının görüntü işleme teknikleri kullanılarak ölçülmesi (Master's thesis, Konya Teknik Üniversitesi).
  • Özçelik, Y. B., & Altan, A. (2021). Diyabetik retinopati teşhisi için fundus görüntülerinin derin öğrenme tabanlı sınıflandırılması. Avrupa Bilim ve Teknoloji Dergisi, (29), 156-167.
  • Yalçin, N., Alver, S., & Uluhatun, N. (2018, May). Classification of retinal images with deep learning for early detection of diabetic retinopathy disease. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  • Budak, D. (2024). Diyabetik retinopati teşhisi için derin öğrenme tabanlı sınıflandırma analizi (Master's thesis, Aksaray Üniversitesi Sosyal Bilimler Enstitüsü).
  • Ağca, K., & Takcı, H. (2022). Hibrit Bir Model Oluşturarak Diyabetik Retinopati Sınıflandırılması. Avrupa Bilim ve Teknoloji Dergisi, (36), 227-236.
  • Ciran, A., & Özbay, E. (2024). Derin Öğrenme ve Özellik Seçimi Yaklaşımları Kullanılarak Göz Hastalıkları Tespiti. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 15(2), 421-433.
  • Ramaha, N., & Imad, S. (2023). Derin Öğrenmeye Karşı Makine Kullanarak Diyabetik Retinopati Teşhisi. Avrupa Bilim ve Teknoloji Dergisi, (51), 301-313.
  • Çavlı, A., & Toğaçar, M. (2023). Yapay Zekâ Yaklaşımlarını Kullanarak Retinopati Hastalığının Tespiti. Mühendislik Bilimleri ve Araştırmaları Dergisi, 5(1), 88-97.
  • Polater, S. N., & Isık, A. H. (2024). Diyabetik Retinopati Tespiti İçin Derin Öğrenmeye Dayalı Sınıflandırma. Uluborlu Mesleki Bilimler Dergisi, 7(2), 13-19.
  • APTOS 2019 blindness detection, 2019, [online] Available: https://www.kaggle.com/c/aptos2019-blindness-detection/data.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
  • Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).
  • Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 (pp. 234-241). Springer international publishing.
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20, 273-297.
  • Schölkopf, B., & Smola, A. J. (2002). Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press.
  • Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1, 81-106.
  • Rokach, L., & Maimon, O. (2005). Decision trees. Data mining and knowledge discovery handbook, 165-192.
  • Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1), 21-27.
  • Telceken, M. (2025). A new feature extraction method for AI based classification of heart sounds: dual-frequency cepstral coefficients (DFCCs). The European Physical Journal Special Topics, 1-12.
There are 32 citations in total.

Details

Primary Language English
Subjects Computer Software, Software Testing, Verification and Validation
Journal Section Research Article
Authors

Muhammed Telçeken 0000-0001-5223-2856

Şeyma Değirmenci 0009-0000-1501-5086

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 Issue: 3

Cite

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 Telçeken M, Değirmenci Ş. Artificial Intelligence-Based Screening for Diabetic Retinopathy: Model Comparison and Interpretability. SAUCIS. September 2025;8(3):510-517. doi:10.35377/saucis.8.94717.1754835
Chicago Telçeken, Muhammed, and Şeyma Değirmenci. “Artificial Intelligence-Based Screening for Diabetic Retinopathy: Model Comparison and Interpretability”. Sakarya University Journal of Computer and Information Sciences 8, no. 3 (September 2025): 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 M. Telçeken and Ş. Değirmenci, “Artificial Intelligence-Based Screening for Diabetic Retinopathy: Model Comparison and Interpretability”, SAUCIS, vol. 8, no. 3, pp. 510–517, 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 (September2025), 510-517. https://doi.org/10.35377/saucis.8.94717.1754835.
JAMA 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, 2025, pp. 510-7, doi:10.35377/saucis.8.94717.1754835.
Vancouver Telçeken M, Değirmenci Ş. Artificial Intelligence-Based Screening for Diabetic Retinopathy: Model Comparison and Interpretability. SAUCIS. 2025;8(3):510-7.
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