An Extensive Analysis on Diabetic Retinopathy Prediction Using Deep Learning Approaches

Abstract

Diabetic Retinopathy leads to loss of vision leads to diabetic mellitus complications. The analysis with computer-aided diagnosis over retinal fundus images is an effectual way for predicting the disease in earlier stage and assists the physicians. There are diverse Machine Learning approaches that are adopted for predicting the complications; however, it leads to computational complexities while performing feature extraction separately. This drawback came be overcome with the utilization of deep learning (DL) approaches as it performs feature extraction and classification concurrently for enhancing the prediction accuracy. This work provides an extensive analysis and reviews on approaches used for DR prediction. There are various challenges that are identified and needs to be addressed using the emergent DL approaches. These approaches are extremely robust, and efficient to predict DR by handling all the learning challenges and provides the direction for further analysis.