Diabetic Retinopathy Detection using MATLAB: MSc-EE Project Report

This project report presents a Master of Science in Electronic Engineering (MSc-EE) project focused on the detection of diabetic retinopathy using MATLAB. The project, undertaken by Hamood Ali Hamood Al-Shamaly, utilizes image processing and artificial intelligence (AI) techniques to analyze fundus images, aiming to identify early signs of diabetic retinopathy, including microaneurysms, exudates, hemorrhages, and optic disc abnormalities. The report outlines the methodology, which includes the use of a V-model software development framework, deep learning AI for pattern recognition, and both black-box and white-box testing. The system's performance was evaluated by an ophthalmologist, achieving approximately 80% accuracy in detecting Non-proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR), as well as segmenting fundus images. The report includes a literature review, system design, testing results, and a critical analysis of the project's strengths, weaknesses, and areas for improvement, such as the use of a larger reference image database and an improved user interface. The project demonstrates the potential of AI-aided digital systems for early diabetic retinopathy detection and provides valuable insights for future clinical applications.