Diabetic Retinopathy Detection Using Machine Learning Techniques and Transfer Learning Approach

Diabetic retinopathy (DR) is a diseased condition of eyes which arises due to prolonged diabetes. It could result in loss of eyesight if not identified and handled in time. Diabetic retinopathy manifests itself as non-proliferative diabetic retinopathy (NPDR) which is the earlier stage and proliferative diabetic retinopathy (PDR) which is the advanced stage. In this study, a machine learning model has been developed that classifies a given fundus image as normal, NPDR, or PDR. Initially, machine learning algorithms like decision trees, Naive Bayes, random forest, K-nearest neighbor (KNN), and support vector machine (SVM) were applied for binary classification, but the classification accuracy was less. So later, we employed transfer learning techniques such as ResNet-50, VGG16, and EfficientNetB0 for binary classification which gave high validation accuracy. Then the above-mentioned transfer learning techniques were further used for multiclass classification which gave very good validation accuracy in tune with the existing research in this field.