  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.
