Deep Learning For Vision Health: An AI-Based Approach For Early Detection of Retinal Diseases

The integration of artificial intelligence into medical diagnostics has revolutionized preventive healthcare, particularly in ophthalmology.This paper introduces a deep learning-based system for the early detection and classification of retinal diseases-namely diabetic retinopathy, glaucoma, and cataracts-using highresolution retinal images.The system employs a multi-stage Convolutional Neural Network (CNN) architecture, initially identifying healthy versus diseased cases, followed by disease-specific classification for more precise diagnostic guidance.The system achieves over 90% accuracy in identifying these conditions, significantly outperforming conventional methods.Designed with speed, accuracy, and accessibility in mind-especially for under-resourced healthcare environments-this system streamlines diagnostic workflows and supports clinical decision-making.It directly contributes to the United Nations Sustainable Development Goals (SDGs), including Good Health and Well-Being, and Industry, Innovation and Infrastructure.