Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)

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Early Detection of Diabetic Retinopathy through GLCM-based Feature Extraction of Microaneurysms and Exudates

Authors
Sharda M Dhavale1, Pushpa M Bangare2, *
1Research Scholar, SPPU University, Sinhgad College of Engineering Vadgaon, Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India
2Sinhgad College of Engineering Vadgaon Pune, Pune, Maharashtra, India
*Corresponding author. Email: pushpa.bangare_skncoe@sinhgad.edu
Corresponding Author
Pushpa M Bangare
Available Online 6 January 2026.
DOI
10.2991/978-94-6463-948-3_52How to use a DOI?
Keywords
Diabetic Retinopathy; Fundus Images; Microaneurysms; Exudates; GLCM; Decision Tree; KNN; Machine Learning
Abstract

Diabetic Retinopathy (DR) is a significant cause of impaired vision on a global scale and primarily characterized by the presence of lesions like microaneurysms and exudates in retinal images. It is imperative to detect such abnormalities when they are at a tender age before they develop serious complications. Here, a computer-based method of detection and classification of DR based on retinal fundus images are introduced. The algorithm involves image preprocessing and segmentation after which there is the texture analysis through Gray Level Co-occurrence Matrix (GLCM). Attributes such as contrast, correlation, energy and homogeneity are obtained and serve as inputs in two machine learning classifiers, Decision Tree (DT) and K-Nearest Neighbor (KNN). The experimental research demonstrates that the given system can distinguish between normal and abnormal cases with reasonable precision. GLCM combined with the machine learning provides a viable path to automated DR screening and can assist ophthalmologists in early diagnosis.

Copyright
© 2025 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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Volume Title
Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
Series
Advances in Intelligent Systems Research
Publication Date
6 January 2026
ISBN
978-94-6463-948-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-948-3_52How to use a DOI?
Copyright
© 2025 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  - Sharda M Dhavale
AU  - Pushpa M Bangare
PY  - 2026
DA  - 2026/01/06
TI  - Early Detection of Diabetic Retinopathy through GLCM-based Feature Extraction of Microaneurysms and Exudates
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 744
EP  - 764
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_52
DO  - 10.2991/978-94-6463-948-3_52
ID  - Dhavale2026
ER  -