Design and Implementation Report: DR Detection using MATLAB and AI

This project report details the design and implementation of a MATLAB-based system for the early detection of diabetic retinopathy (DR). The project, submitted for a Master of Science in Electronic Engineering, focuses on utilizing AI and deep learning techniques to analyze fundus images captured via a smartphone. The report outlines the development process, including the use of the V-model software development framework, hardware and software design, system architecture, and testing methodologies. The system aims to segment fundus images, identifying key indicators such as microaneurysms, exudates, hemorrhages, and optic disc anomalies, to differentiate between Non-proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR) stages. The report includes a literature review, project budget and schedule, and a critical analysis of the system's performance and potential improvements, including the use of a larger database of reference images and a user guide. The developed system achieved approximately 80% accuracy in detecting NPDR and PDR and segmenting fundus images.