Title : RETINAL IMAGE ANALYSIS FOR DIABETIC RETINOPATHY DETECTION
Author : Dr. P. S. Naveen Kumar, Poda Harsha Vardhan, Rangoju Raghunadha Chari, Shaik Rajiyabee
Abstract :
Diabetic retinopathy (DR) is a severe complication of diabetes affecting the retina, leading to vision impairment and blindness if untreated. Early detection of DR is crucial for preventing vision loss and enabling timely intervention. Manual screening of retinal fundus images by ophthalmologists is time-consuming and subject to inter-observer variability. This research presents an automated retinal image analysis system for diabetic retinopathy detection using deep learning. The proposed framework leverages convolutional neural networks (CNNs) to extract discriminative features from retinal images. Preprocessing techniques such as image enhancement and normalization improve input quality. The model is trained on a large dataset of labeled retinal images representing different DR severity levels. Experimental results demonstrate high sensitivity and specificity in identifying DR conditions. Automated grading of DR stages enhances clinical decision-making. The system reduces dependency
