General Deep Learning Model for Detecting Diabetic Retinopathy

Abstract

Diabetic retinopathy (DR) is a common and potentially sight-threatening complication of diabetes. Early detection and accurate diagnosis of DR are crucial for effective treatment and prevention of vision loss. In recent years, deep learning techniques have shown remarkable success in various medical imaging tasks. This research paper presents a general deep learning model designed specifically for detecting diabetic retinopathy in retinal images. The proposed model leverages convolutional neural networks (CNNs) to automatically extract relevant features from retinal images and classify them into different stages of DR severity. Experimental results demonstrate the effectiveness and robustness of the proposed model in accurately detecting DR, showcasing its potential for clinical deployment.