Diabetic retinopathy identification system based on transfer learning

Abstract

Abstract Diabetic retinopathy, a complication of diabetes, is a significant cause of vision loss and blindness. Early detection of diabetic retinopathy can help reduce the risk of blinding. However, automatic diabetic retinopathy identification is a challenging task due to their different morphology during a different stage. Aiming at the problem of low efficiency for most of the existing methods, we developed a diabetic retinopathy recognition system based on transfer learning. The system utilizes transfer learning, which trains a neural network based on the DenseNet201 network model and Messidor Data Set, and can not only train an active network quickly, but also has a reasonable effect on the classification of diabetic retinopathy.