Data Augmentation using Generative Adversarial Networks for Pneumonia classification in chest Xrays

In medical images, data augmentation is essentially important for accurate classification of images especially when available data is limited. This paper proposes a noble data augmentation method of generating synthetic chest Xray images of patients with Pneumonia using Generative Adversarial Networks (GANs). The proposed model first uses conventional data augmentation techniques along with GANs to generate more training samples. A specific implementation of GANs allows us to produce unprecedented Chest X-Ray images of patients suffering from Pneumonia. The generated samples are then used to train a DCNN model to classify chest X-Ray images. The classifier significantly improves its accuracy after the introduction of synthetic data produced by the GAN model.