A Hybrid CNN-GNN Framework for Enhanced Glaucoma Detection Using Retinal Fundus Images

S Hemalatha, S R Khrisha, K S Praneetheaswarr, M Sibi Logesh

2025

Abstract

Convolutional neural networks are very powerful tools used for the analysis of structured Euclidean space data. However, it is on applications like image classification and audio analysis, language processing among others. These models, therefore, can effectively retrieve critical features necessary for deciding. However, many real-life problems involve data organized along non-Euclidean geometries, including social networks and medical imaging, genetic studies among others. In these contexts, relationships of data points become important. Particularly in medical image classification, using semantic relationships of features within images improves detection accuracy of complex diseases considerably. Graph Neural Networks are proficient at modeling relational data in such cases using graph structures. This paper presents a novel deep learning framework that combines CNNs and GNNs, combining their complementary strengths. It utilizes CNNs for feature extraction and GNNs for modeling relationships between features to offer a robust approach to glaucoma detection from retinal fundus images. Using the ORIGA dataset, we designed a three-component architecture: Feature Extractor, Graph Constructor, and Graph Classifier. Our experiments explored several techniques of graph construction and similarity-measuring techniques, which lead to better classification performance. Our proposed CNN-GNN ensemble was able to reach precision at 0.79, recall at 0.76, and F1 score at 0.77, outperforming previous approaches.

Paper Citation

in Harvard Style

Hemalatha S., Khrisha S., Praneetheaswarr K. and Sibi Logesh M. (2025). A Hybrid CNN-GNN Framework for Enhanced Glaucoma Detection Using Retinal Fundus Images. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 598-602. DOI: 10.5220/0013632900004664

in Bibtex Style

@conference{incoft25,
author={S Hemalatha and S R Khrisha and K S Praneetheaswarr and M Sibi Logesh},
title={A Hybrid CNN-GNN Framework for Enhanced Glaucoma Detection Using Retinal Fundus Images},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={598-602},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013632900004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - A Hybrid CNN-GNN Framework for Enhanced Glaucoma Detection Using Retinal Fundus Images
SN - 978-989-758-763-4
AU - Hemalatha S.
AU - Khrisha S.
AU - Praneetheaswarr K.
AU - Sibi Logesh M.
PY - 2025
SP - 598
EP - 602
DO - 10.5220/0013632900004664
PB - SciTePress