Development Of Eye Disease Detection Using Deep Learning

Abstract: Catching eye diseases early stops patients from going blind. Right now, doctors check eye photos by hand. This manual process takes too much time and causes frequent mistakes. This paper proposes a robust computer- aided diagnosis (CAD) system designed to automatically classify retinal fundus images into eight distinct categories: Normal, Glaucoma, Diabetes, Cataracts, Age-related Macular Degeneration (AMD), Hypertension, Pathological Myopia, and other abnormalities. Utilizing a Multi-class Fundus Image Dataset, the research implements a deep learning framework centered on Convolutional Neural Networks (CNNs). The methodology integrates advanced image preprocessing—including grayscale conversion, noise filtration, and contrast-limited adaptive histogram equalization (CLAHE)—to enhance diagnostic features. The system is deployed via a high-performance web interface, ensuring low computational overhead and seamless user interaction. Experimental results indicate high precision and recall, demonstrating the system’s efficacy in facilitating rapid, large-scale ocular screenings.

Keywords: Deep Learning, Convolutional Neural Networks (CNN), Retinal Fundus Imaging, Computer-Aided Diagnosis (CAD), Medical Image Processing, Ocular Pathology