Analysis of Transfers Learning Techniques for Early Detection and Grading of Diabetic Retinopathy on Retinal Images

Eye complications of diabetes include diabetic retinopathy (DR). It's caused by retinal blood vessel alterations. Working-age individuals' major cause of blindness is diabetic retinopathy. Early identification of diabetic retinopathy may prevent or postpone visual loss. Artificial intelligence (AI) can use computational image analysis methods to automatically identify and categorize diabetic retinopathy in retinal pictures. In this paper we study and analyze the different transfer learning models for early detection of diabetic retinopathy for retinal images. According to our findings, the accuracy of predictions ranges from 74% to 81%. It is worth noting that AI-based systems for diabetic retinopathy detection are still in the research phase, and more research is needed to evaluate their accuracy and effectiveness in real-world settings.