Deepfakes on Retinal Images using GAN

Abstract

In Deep Learning (DL), Generative Adversarial Networks (GAN) are a popular technique for generating synthetic images, which require extensive and balanced datasets to train. These Artificial Intelligence systems can produce synthetic images that seem authentic, known as Deep Fakes. At present, datadriven approaches to classifying medical images are prevalent. However, most medical data is inaccessible to general researchers due to standard consent forms that restrict research to medical journals or education. Our study focuses on GANs, which can create artificial fundus images that can be indistinguishable from actual fundus images. Before using these fake images, it is essential to investigate privacy concerns and hallucinations thoroughly. As well as, reviewing the current applications and limitations of GANs is very important. In this work, we present the Cycle-GAN framework, a new GAN network for medical imaging that focuses on the generation and segmentation of retinal fundus images.DRIVE retinal fundus image dataset is used to evaluate the proposed model’s performance and achieved an accuracy of 98.19%.