A Study on Various Classifications and Prediction Techniques for Diabetic Retinopathy

Diabetic retinopathy is one of the most common causes of blindness worldwide. The future of diabetic retinopathy is brightening on a worldwide scale. The feature selection and classification is remaining as a critical task for determining the severity of diabetic retinopathy. Various studies have used different methodologies to extract the characteristics and categorize diabetic retinopathy. Moreover, different techniques are used for screening, decision making and performing future prediction for Diabetic retinopathy. The main comparison metrics of Diabetic Retinopathy prediction are accuracy, sensitivity, and specificity. This paper presents various techniques related to the features, classification and prediction using artificial intelligence, machine learning and Deep learning techniques. The entire paper consists of various classification techniques, advantages, limitations and various comparison parameters. Finally, various challenges and future direction of the Diabetic retinopathy is presented.