Robust Classification Model for Diabetic Retinopathy Based on the Contrastive Learning Method with a Convolutional Neural Network

Abstract

Diabetic retinopathy is one of the most common microvascular complications of diabetes. Early detection and treatment can effectively reduce the risk. Hence, a robust computer-aided diagnosis model is important. Based on the labeled fundus images, we build a binary classification model based on ResNet-18 and transfer learning and, more importantly, improve the robustness of the model through supervised contrastive learning. The model is tested with different learning rates and data augmentation methods. The standard deviations of the multiple test results decrease from 4.11 to 0.15 for different learning rates and from 1.53 to 0.18 for different data augmentation methods. In addition, the supervised contrastive learning method also improves the average accuracy of the model, which increases from 80.7% to 86.5%.