Data Augmentation using Generative Adversarial Networks (GANs) for GAN-based Detection of Pneumonia and COVID-19 in Chest X-ray Images

TL;DR

This paper introduces a novel GAN architecture for augmenting chest X-ray data, enhancing the detection accuracy of pneumonia and COVID-19, and surpassing traditional augmentation methods.

Contribution

A new GAN model tailored for chest X-ray augmentation that improves disease detection accuracy over existing methods.

Findings

GAN-based augmentation outperforms traditional methods

Improved classification accuracy for pneumonia and COVID-19

GAN augmentation surpasses Deep Convolutional GAN in experiments

Abstract

Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively. Standard data augmentation methods, however, produce limited plausible alternative data. Generative Adversarial Networks (GANs) have been utilized to generate new data and improve the performance of CNNs. Nevertheless, data augmentation techniques for training GANs are under-explored compared to CNNs. In this work, we propose a new GAN architecture for augmentation of chest X-rays for semi-supervised detection of pneumonia and COVID-19 using generative models. We show that the proposed GAN can be used to effectively augment data and improve classification accuracy of disease in chest X-rays for pneumonia and COVID-19. We compare our augmentation GAN model with Deep Convolutional GAN and traditional augmentation methods (rotate, zoom, etc) on two different X-ray datasets and show our GAN-based augmentation method surpasses other augmentation methods for training a GAN in detecting anomalies in X-ray images.