Retinal Degeneration using High Resolution Fundus Images

Authors: Janhvi Dipak Gore, Gayatri Suresh Purkar, Geeta Tukaram Yelmame, Tanvi Rajesh Nimbalkar, R. M. Gawande

Country: India

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Abstract: Early detection of retinal diseases is essential to prevent vision loss and reduce the burden on healthcare systems. However, traditional diagnostic methods are time-consuming, require expert ophthalmologists, and are not easily accessible in rural or resource-limited areas. This paper presents an intelligent eye disease detection system based on high-resolution fundus images using a hybrid machine learning approach. The proposed system combines Convolutional Neural Networks (CNN) for feature extraction and Support Vector Machine (SVM) for accurate classification of retinal diseases such as Diabetic Retinopathy and Glaucoma. The system includes image preprocessing techniques to enhance image quality and a heatmap generation mechanism to highlight affected regions of the retina, improving interpretability for medical professionals. A web-based platform is developed to allow users to upload retinal images and receive instant diagnostic results. The proposed solution aims to provide fast, reliable, and accessible screening, especially in remote areas. Experimental results demonstrate that the system achieves high accuracy while maintaining real-time performance, making it suitable for large-scale retinal disease detection and early diagnosis.

Keywords: Retinal Disease Detection, Fundus Image Analysis, Convolutional Neural Network (CNN), Support Vector Machine (SVM), Hybrid Model, Deep Learning, Medical Image Processing, Heatmap Visualization, Web-Based Diagnosis, Early Detection of Eye Diseases.

Paper Id: 233124

Published On: 2026-05-21

Published In: Volume 14, Issue 3, May-June 2026

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