India has over 77 million diabetic adults - the second highest globally. Diabetic Retinopathy (DR) affects ~18% of this population and is a leading cause of preventable blindness. Early screening can prevent90% of vision loss, but India has only ~1 ophthalmologist per 100,000 rural population, making mass manual screening infeasible. Existing AI solutions function as black boxes, lack clinical validation rigor, and fail with variable image quality from portable fundus cameras in field conditions
Design a MATLAB-based retinal image analysis pipeline for automated DR screening addressing real-world deployment challenges: 1.
India has over 77 million diabetic adults - the second highest globally Diabetic Retinopathy (DR) affects ~18% of this population and is a leading cause of preventable blindness Early screening can prevent90% of vision loss, but India has only ~1 ophthalmologist per 100,000 rural population, making mass manual screening infeasible
Design a MATLAB-based retinal image analysis pipeline for automated DR screening addressing real-world deployment challenges: 1. Image Quality Assessment and Enhancement: Automatically evaluate fundus images for adequacy (focus, illumination, field of view). Apply adaptive enhancement (CLAHE, illumination normalization, denoising) for borderline images; reject ungradeable ones with recapture feedback. 2. Retinal Structure Segmentation: Extract clinically relevant structures - optic disc/fovea localization, vessel segmentation, microaneurysm detection, exudate segmentation, hemorrhage classification, and neovascularization detection. 3. DR Severity Grading: Classify using the International Clinical DR severity scale (Levels 0-4, from no DR to proliferative DR) with clinically acceptable sen