Ai-Based Detection Of Diabetic Retinopathy From Fundus Images

Abstract

Early detection of Diabetic Retinopathy (DR) is a hot topic among researchers. Various Artificial Intelligence (AI) techniques have been employed to screen and diagnose DR early on, aiming to protect diabetic patients from blindness, which is often linked to the severity of the condition. Unfortunately, many existing models fall short due to time inefficiencies and premature convergence, which pose significant challenges in real-world applications. In light of these limitations, three key gaps have emerged: first, there's a critical lack of available medical data; second, there's a need for enhanced optimization algorithms to prioritize and select features for improved outcomes; and finally, relying on a single algorithm can be limiting when a combination of algorithms might yield better results. To tackle these challenges, this research proposes a novel computerized model for DR identification that incorporates three innovative methodologies. Diabetic Retinopathy is a leading cause of blindness among diabetic patients, primarily because it often shows no symptoms until significant damage has occurred. Early detection of this disease can help prevent blindness. Accurately identifying key features in fundus images, such as the optic disc, is crucial. Timely recognition of signs like hemorrhages, optic disc changes, exudates, cotton wool spots, and microaneurysms can slow the progression of the disease and assist doctors in providing effective treatment.