Evaluating Deep Learning Resilience in Retinal Fundus Classification with Generative Adversarial Networks Generated Images

Abstract

The evaluation of Generative Adversarial Networks in the medical domain has shown significant potential for various applications, including adversarial machine learning on medical imaging. This study specifically focuses on assessing the resilience of Convolutional Neural Networks in differentiating between real and Generative Adversarial Network-generated retinal images. The main contributions of this research include the training and testing of Convolutional Neural Networks to evaluate their ability to distinguish real images from synthetic ones. By identifying networks with optimal performances, the study ensures the development of better models for diagnostic classification, enhancing generalization and resilience to adversarial images. Overall, the aim of the study is to demonstrate that the application of Generative Adversarial Networks can improve the resilience of the tested networks, resulting in better classifiers for retinal images. In particular, a network developed by authors, i.e., Standard_CNN, reports the best performance with accuracy equal to 1.