Data Augmentation using Generative Adversarial Network with Enhanced Deep Convolutional Neural Network for Pneumonia Chest X-ray Images

Abstract Pneumonia is a serious and life-threatening disease that results in difficulty breathing and needs to be diagnosed as soon as possible. Chest Xray is commonly used to diagnose pneumonia because they are cheap and readily available. Therefore the role of artificial intelligence in radiology has become a hot topic in using deep learning models. To train this model, a huge amount of datasets is required. To overcome this issue data augmentation technique has become a popular method for boosting the training data, particularly in medical images, where the data availability is insufficient. The generative adversarial network has become a popular method to generate synthetic data that looks like real data to increase the model’s performance compared to the traditional augmentation method. This paper focuses on a new GAN architecture i.e, Pneumonia GAN (a.k.a PGAN), to augment chest xray images using generative models. This novel PGAN architecture can successfully increase xray images. The performance of the novel PGAN model generates xray images that look like real images when compared with a conventional method. The comparison result proved that the proposed model generates an excess number of images compared to other techniques, and the mathematical calculation for each layer is discussed.