A Survey on Artificial Intelligence and Deep Learning Techniques for Diabetic Retinopathy Detection and Classification

DOI

10.2991/978-94-6239-616-6_18How to use a DOI?

Keywords

Diabetic Retinopathy; Fundus Imaging; Vision Transformer; Convolutional Neural Networks; Explainable AI; Deep Learning; Classification

Abstract

Diabetic Retinopathy (DR) is a leading cause of preventable blindness, and early diagnosis remains challenging due to subtle lesion patterns and the need for expert-grade annotations. Recent advances in Artificial Intelligence—particularly Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs)—have significantly improved automated DR screening accuracy. This survey provides a structured analysis of existing machine learning and deep learning approaches, highlights commonly used datasets, preprocessing strategies, and evaluation metrics, and synthesizes comparative results across major studies. In addition to summarizing current progress, the survey identifies limitations including dataset imbalance, poor model interpretability, and privacy concerns in clinical deployment. Future directions emphasize ViT–XAI integration, multi-modal fundus–OCT frameworks, federated ViT architectures, and privacy-preserving learning to enable reliable, scalable, real-time DR diagnosis.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - C. Vanaja
AU  - R. Harikanth
AU  - A. Sabarinath
AU  - S. Naveenkumar
PY  - 2026
DA  - 2026/03/31
TI  - A Survey on Artificial Intelligence and Deep Learning Techniques for Diabetic Retinopathy Detection and Classification
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 212
EP  - 222
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_18
DO  - 10.2991/978-94-6239-616-6_18
ID  - Vanaja2026
ER  -