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

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