A lightweight classification system for the early detection of diabetic retinopathy

The eye disease known as Diabetic Retinopathy is one of the leading causes of permanent blindness in people of working age worldwide. Early identification is crucial for the treatment and management of the condition and this study presents a trustworthy approach for identifying the early stages of the disease from fundus images. A comparative analysis of a supervised machine learning algorithm and manual classification conducted by qualified optometrists is used to evaluate the work. Diabetic Retinopathy features such as Hard Exudates, Microaneurysms and Blood Vessels are extracted from the retinal images by a number of feature extraction methods. The performance and robustness of the proposed novel system are assessed using confusion matrix data and AUC-ROC curves. The findings demonstrate the validity of the decision-based system for the early detection of diabetic retinopathy, with the potential to be deployed on a portable screening system that can be used by people living in remote areas of the world.