Automated Retinal Analysis Using Deep Convolutional Networks

Abstract

Diabetes-induced retinal damage (DR) represents a notable vision threatening problem that needs initial identification because it is essential to prevent irrecoverable damage and blindness. Through this paper we propose a machine learning approach for diabetic retinopathy using deep learning. Retinal fund images are analyzed using a Convolutional Neural Network (CNN) based on the residual network 152 architecture and categorize them in three stages of DR severity level. This model was trained with the help of in-depth dataset with marked retinal images, resulting in an accuracy rate of approximately 97% on the test set. The system includes a user-friendly interface for seamless integration in clinical settings. Our approach demonstrates the feasibility of automated DR screening. This may support faster, more reliable, and scalable diagnostic methods for early intervention and improved patient outcomes.