Convolution Neural Network Based Approach for Diabetic Retinopathy Detection using Fundus Images

Abstract

Abstract: Diabetic Retinopathy (DR) represents the most prevalent complication arising from diabetes, impacting the retina and standing as a leading cause of global blindness. Timely detection plays a pivotal role in preserving patients' vision, yet early identification remains challenging, relying heavily on clinical experts' interpretation of fundus images. In this investigation, a deep learning model underwent training and validation using a proprietary dataset. The intelligent model assessed the quality of test images, distinguishing them into DR-Positive and DR-Negative categories and further classifying their severity stages, encompassing mild, moderate, severe, and normal. Subsequently, expert review will scrutinize the model's performance based on the obtained results