Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)

<Previous Article In Volume
Next Article In Volume>

Comprehensive Analysis of Artificial Intelligence Techniques for Diabetic Retinopathy Disease Detection

Authors
Aanchal Mehta1, Vandana Bajaj2, *, Rattan Deep Aneja3
1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
2Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
3Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
*Corresponding author. Email: Vandana.bajaj@chitkara.edu.in
Corresponding Author
Vandana Bajaj
Available Online 16 June 2026.
DOI
10.2991/978-94-6239-693-7_27How to use a DOI?
Keywords
Artificial Intelligence; Machine Learning; Deep Learning; Image Processing; Diabetic Retinopathy
Abstract

Machine Learning (ML) and Deep Learning (DL) have emerged as powerful techniques in many fields, including speech recognition, cybersecurity, text generation, financial fraud detection and medical image analysis. There are numberless Image Processing (IP) techniques used for processing, analyzing and extracting information from images. The ML and DL models rely on a centralized system for disease prediction on medical imaging. Medical image processing exposes a non-invasive strategy for disease screening. However, to collect medical records at a centralized location, leading to data dependency, adversarial vulnerability, high computational resources and data storage concerns. Diabetic Retinopathy (DR) is a vision threatening disease in human beings. If DR is not identified in an early stage or not treated timely, the damage is irreversible. The diagnosis of DR disease is challenging, especially with limited resources, time-consuming and dependent on the ophthalmitis experiences. The research highlight has ML and DL models showing strong potential in DR disease detection, grading, segmentation, feature extraction, classification, diagnosis and even prediction in early stages on retinal images.

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.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
16 June 2026
ISBN
978-94-6239-693-7
ISSN
2589-4919
DOI
10.2991/978-94-6239-693-7_27How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - Aanchal Mehta
AU  - Vandana Bajaj
AU  - Rattan Deep Aneja
PY  - 2026
DA  - 2026/06/16
TI  - Comprehensive Analysis of Artificial Intelligence Techniques for Diabetic Retinopathy Disease Detection
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 258
EP  - 264
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_27
DO  - 10.2991/978-94-6239-693-7_27
ID  - Mehta2026
ER  -