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IJIEE
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Published: 2025-03-24

Diabetic Retinopathy Detection using Federated Learning and Vision Transformers

Associate Professor, Dept. of Computer Science and Engineering Stanley College of Engineering and Technology for Women, Hyderabad
Student, Final Year, Dept. of Computer Science and Engineering National Institute of Technology, Warangal
Student, Final Year, Dept. of Computer Science and Engineering National Institute of Technology, Warangal
Diabetic Retinopathy Federated Learning Vision Transformers Large Language Models Medical Image Analysis Data Privacy

Abstract

Early detection of Diabetic retinopathy (DR) is essential for preventing blindness because this disease currently stands as the primary cause of blindness. The implementation of traditional deep learning models becomes difficult because data privacy risks alongside insufficient dataset availability among different healthcare institutions. We introduce an FL-ViT cooperative model training framework which supports distributed information processing without requiring shared clinical data ownership. The use of ViTs in self-attention feature extraction from retinal images together with FL technology guarantees privacy standards compliance. Healthcare operators use a Model designed for the sector to produce diagnostic documents that speed up clinical operations. The system achieves 93 percentage accuracy, ROC curve is 0.89 , precision and recall and confusion matrix  in DR grading according to APTOS dataset evaluations. The solution resolves issues pertaining to AI scalability and generalizability along with compliance with healthcare ethical requirements.

References

  1. Dai, L., Wu, L., Li, H., Cai, C., Huang, Q., Nguyen, T. V., ... & Wang, J. (2021). A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nature communications, 12(1), 3242.
  2. Chetoui, M., & Akhloufi, M. A. (2023). Federated learning using vision transformer for diabetic retinopathy detection. Biomedical Signal Processing and Control, 79, 104081.
  3. Fayyaz, Z., Mohammadian, N., Tabar, M. R. R., Mansoori, S., & Mahloo-jifar, A. (2023). A comparative study of deep learning methods for dia-betic retinopathy detection. Biomedical Signal Processing and Control, 80, 104359.
  4. Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Poor, H. V. (2022). Federated learning for COVID-19 detection with generative ad-versarial networks in edge cloud computing. IEEE Internet of Things Jour-nal, 9(21), 21266-21278.
  5. Uppamma, S., Gopi, V. P., & Palanisamy, P. (2023). Federated learning-based deep convolutional neural network for diabetic retinopathy detection and grading. Biomedical Signal Processing and Control, 84, 104621.
  6. Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., ... & Poor, H. V. (2023). Federated learning with differential privacy: Algorithms and performance analysis. IEEE Transactions on Information Forensics and Security, 15, 3454-3469.
  7. Yi, X., Walia, E., & Babyn, P. (2022). Unsupervised and semi-supervised learning with categorical generative adversarial networks assisted by self-supervised learning for brain tumor segmentation. Biomedical Signal Pro-cessing and Control, 71, 103107.
  8. Moshawrab, M., Aloqaily, M., Boukerche, A., & Bouachir, O. (2023). Fed-erated learning in healthcare: Concepts, applications, challenges, and future directions. ACM Computing Surveys, 55(8), 1-37.
  9. Sebastian, P., Voon, Y. V., & Comley, R. (2023). Federated learning-based real-time diabetic retinopathy detection. Neural Computing and Applica-tions, 35(9), 6669-6685.
  10. Atwany, F., Sahyoun, A., & Yaqub, M. (2022). Multi-classification of di-abetic retinopathy using deep learning. Biomedical Signal Processing and Control, 73, 103452.
  11. https://www.kaggle.com/c/diabetic-retinopathy-detection
  12. Decenci` ere, E., Zhang, X., Cazuguel, G., La¨y, B., Cochener, B., Trone, C., ... & Klein, J. C. (2014). Feedback on a publicly distributed image database: the Messidor database. Image Analysis & Stereology, 33(3), 231-234.
  13. Abr` amoff, M. D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J. C., & Niemeijer, M. (2016). Improved automated detection of diabetic retinopa-thy on a publicly available dataset through integration of deep learning. In-vestigative ophthalmology & visual science, 57(13), 5200-5206.
  14. Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sa-hasrabuddhe, V., & Meriaudeau, F. (2018). Indian diabetic retinopathy im-age dataset (IDRiD): a database for diabetic retinopathy screening research. Data, 3(3), 25.
  15. Kaggle. (2019). APTOS 2019 Blindness Detection. Retrieved from https://www.kaggle.com/c/aptos2019-blindness-detection
  16. Dai, L., Wu, L., Li, H., Cai, C., Huang, Q., Nguyen, T. V., ... & Wang, J.
  17. (2021). A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nature communications, 12(1), 3242.
  18. Galtier, Mathieu, and Darius Meadon. "Applying AI to real-world health-care settings and the life sciences: Tackling data privacy, security and policy challenges with federated learning." Artificial Intelligence in Science Challenges, Opportunities and the Future of Research: Challenges, Opportunities and the Future of Research (2023): 170.
  19. Chetoui, M., & Akhloufi, M. A. (2023). Federated learning using vision transformer for diabetic retinopathy detection. Biomedical Signal Processing and Control, 79, 104081.
  20. Halder, Arindam, et al. "Implementing vision transformer for classifying 2D biomedical images." Scientific Reports 14.1 (2024): 12567.
  21. Alwakid, Ghadah, Walaa Gouda, and Mamoona Humayun. "Deep Learning-based prediction of Diabetic Retinopathy using CLAHE and ESRGAN for Enhancement." Healthcare. Vol. 11. No. 6. MDPI, 2023.
  22. Almufareh, Maram Fahaad, et al. "A federated learning approach to breast cancer prediction in a collaborative learning framework." Healthcare. Vol. 11. No. 24. MDPI, 2023.
  23. Obayya, Marwa, et al. "Explainable artificial intelligence enabled TeleOphthalmology for diabetic retinopathy grading and classification." Applied Sciences 12.17 (2022): 8749.
  24. arXiv. (2024). Federated Learning in Healthcare: Model Misconducts, Security Challenges, and Future Directions.
  25. Barkmeier, Andrew J. "Toward optimal screening for diabetic retinopathy: Balancing precision and pragmatism." Mayo Clinic Proceedings. Vol. 96. No. 2. Elsevier, 2021.
  26. Rieke, Nicola, et al. "The future of digital health with federated learning." NPJ digital medicine 3.1 (2020): 1-7.
  27. Nguyen, Giang, et al. "Landscape of machine learning evolution: privacy-preserving federated learning frameworks and tools." Artificial Intelligence Review 58.2 (2024): 51.
  28. Restrepo, David, et al. "Representation Learning of Lab Values via Masked AutoEncoder." arXiv preprint arXiv:2501.02648 (2025).
  29. Tymchenko, Borys, Philip Marchenko, and Dmitry Spodarets. "Deep learning approach to diabetic retinopathy detection." arXiv preprint arXiv:2003.02261 (2020).
  30. Almufareh, Maram Fahaad, et al. "A federated learning approach to breast cancer prediction in a collaborative learning framework." Healthcare. Vol. 11. No. 24. MDPI, 2023.
  31. Symeonides, Moysis, Demetris Trihinas, and Fotis Nikolaidis. "FedMon: A Federated Learning Monitoring Toolkit." IoT 5.2 (2024): 227-249.
  32. Zhao, Joshua C., et al. "Federated Learning Privacy: Attacks, Defenses, Applications, and Policy Landscape-A Survey." arXiv preprint arXiv:2405.03636 (2024).
  33. Uriawan, Wisnu, et al. "Challenges and opportunities: improve patient data security and privacy in distributed systems." (2024).
  34. Ali, Md Shahin, et al. "Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions--A Systematic Review." arXiv preprint arXiv:2405.13832 (2024).
  35. Zhao, Joshua C., et al. "Federated Learning Privacy: Attacks, Defenses, Applications, and Policy Landscape-A Survey." arXiv preprint arXiv:2405.03636 (2024).
  36. Henry, Emerald U., Onyeka Emebob, and Conrad Asotie Omonhinmin. "Vision transformers in medical imaging: A review." arXiv preprint arXiv:2211.10043 (2022).

How to Cite

Dr. M. Swapna*, Tanishqa Ravirala, & Nikhitha Reddy. (2025). Diabetic Retinopathy Detection using Federated Learning and Vision Transformers. International Journal of Interpreting Enigma Engineers (IJIEE), 2(1), 10–21. Retrieved from https://ejournal.svgacademy.org/index.php/ijiee/article/view/136

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