Deep Dive Into Diabetic Retinopathy Identification A Deep Learning Approach With Blood Vessel Segmentation And Lesion Detection

Abstract

Diabetic Retinopathy (DR) is one of the leading causes of vision impairment and blindness among diabetic patients worldwide. Early detection and accurate diagnosis are critical to prevent irreversible vision loss. Traditional manual screening methods are time-consuming, subjective, and heavily dependent on ophthalmological expertise, making them unsuitable for large-scale screening. This project presents a deep learning–based automated system for diabetic retinopathy identification by integrating blood vessel segmentation and lesion detection techniques using convolutional neural networks. The proposed system processes retinal fundus images through preprocessing, vessel segmentation, and lesion detection stages to improve diagnostic accuracy and reliability. By leveraging deep learning models, the system aims to provide faster, consistent, and cost-effective DR detection, supporting clinicians in early diagnosis and enabling timely medical intervention. Keywords: Diabetic Retinopathy, Deep Learning, Blood Vessel Segmentation, Lesion Detection.