Detection algorithm for diabetic retinopathy based on ResNet and transfer learning

DR (Diabetic retinopathy) a chronic progressive disease which affects eyesight and even causes blindness. It is significance to carry out the identification and severity diagnosis of DR, timely diagnosis and treatment of DR Patients, improve the people’s quality, especially the elderly, and improve the efficiency of diagnosis. In this study, with the goal of efficient and accurate division of DR Levels, a DR Recognition and classification algorithm based on ResNet and transfer learning is proposed. Firstly, shallow feature extraction module of ResNet18 is used to get retinal image feature, and then the fully connected classification structure model of DR Is designed. Then the transfer learning method is combined to train the network weights to improve the generalization ability of the model, ResNet-18 is selected as the backbone network model for feature extracting. Results show that the accuracy of the training set reaches to provide useful guidance for DR Automatic diagnosis, and effectively alleviates the problem of low accuracy of DR Classification