Combining ResNet and RBF Networks to Enhance the Robustness of Diabetic Retinopathy Detection Against FGSM Attacks

Diabetic Retinopathy (DR) is a common complication of diabetes that can lead to vision loss if not detected early. Deep learning models, particularly Convolutional Neural Networks (CNN), have shown impressive results in DR detection. However, these models are susceptible to adversarial attacks, such as the Fast Gradient Sign Method (FGSM), leading to misclassification of detection data. In order to improve the robustness of the Diabetic Retinopathy (DR) detection model against FGSM attacks, we propose a new neural network in this paper that combines the Radial Basis Function (RBF) network with the modern CNN ResNet. We first trained ResNet-101 and the proposed ResNet-101-RBF on the aptos2019-blindness-detection dataset. After training, we evaluated the performance of the two models on normal DR data, and next, we evaluated the performance of the two models on adversarial data generated by FGSM attacks. Our results show that the accuracy of ResNet-101 in normal data is 78.26%, and the accuracy of ResNet-101-RBF is 75.26%, with the accuracy of ResNet-101 being 3% higher than that of ResNet-101-RBF in normal data. However, on adversarial samples generated with perturbation rates of 0.01, 0.05, and 0.1, the accuracy of ResNet-101-RBF is 6%, 5.5%, and 4.75% higher than that of ResNet-101, respectively. The ResNet-101-RBF model can maintain a high detection accuracy in normal data while having higher robustness against FGSM attacks compared to the ResNet-101 model.