Advancements in Diabetic Retinopathy Detection: Analyzing the Efficacy of Supervised Learning Algorithms

Abstract

Diabetic retinopathy (DR) is a severe complication of diabetes mellitus and a leading 
cause of vision impairment and blindness globally. Early detection and accurate classification of 
DR are critical for preventing severe vision loss. Automated detection systems using supervised 
learning algorithms have significantly improved the accuracy and efficiency of DR diagnosis and 
have revolutionized DR detection by leveraging fundus images to classify the severity of the 
disease. The focus has also expanded to not only detecting the presence of DR but also assessing 
its severity, thereby enabling tailored treatment strategies. Techniques like transfer learning and 
ensemble models have shown great promise in refining predictions and addressing the challenges 
of limited labeled data.