Generating High-Resolution Chest X-ray Images Using CGAN

Abstract

Deep Learning (DL) models have outperformed remarkably and effectively on several Computers Vision applications. However, these models require large amounts of data to avoid overfitting problems. Overfitting happens when a network trains a function with an incredibly high variance to represent the training data perfectly. Consequently, medical images lack to availability of large labeled datasets, and the annotation of medical images is expensive and time-consuming for experts, as the COVID-19 virus is an infectious disease, these datasets are scarce and it is difficult to get large datasets. The limited amount of the COVID-19 class compared to any other classes, for example (healthy). To solve the scarcity data problem, we adjust a Conditional Generative Adversarial Network (CGAN) as a solution to the problems of scarcity and limited data. CGAN contains two neural networks: a generator that creates synthetic (fake) images, and a discriminator that recognizes a real sample of training and a generated sample from the generator. The adjusted CGAN is able to Generate synthetic images with high resolution and close to the original images which aid in expanding the limited dataset specific to a new pandemic. In addition to CGAN augmenting strategies, this research also briefly explores additional aspects of data augmentation like time augmentation and total dataset size. Frechet inception distance metric (FID) has been used for evaluating synthetic images generated by CGAN. The adjusted CGAN obtains better FID results for the high-resolution synthetic X-rays images it achieves 2.349%.