Generating Chest X-Ray Progression of Pneumonia Using Conditional Cycle Generative Adversarial Networks

Abstract

Pneumonia is an inflammation of the lungs caused by pathogens or autoimmune diseases, with about 450 million patients worldwide each year. Chest X–ray analysis is the most common radiographic method used to diagnose pneumonia, and advances in deep learning have led to the availability of high-dimensional image, audio, and video data. Deep learning is being applied in many fields, including the medical field, where numerous researchers have attempted to use it for computer-aided diagnosis. Recently, with the appearance of generative adversarial networks, it is possible to generate plausible and realistic images. In this paper, we combined cycle Generative Adversarial Networks (GANs) and conditional GANs, which are extensions of GANs, to convert the domains between images and generate images of the intermediate domains. We conducted the domain change between pneumonia images and normal images by applying our framework to a Chest X–ray image dataset. We evaluated the domain change by redefining the ResNet152-based classifier, and we generated the pneumonia progression images by inputting a value between two domains in the conditional vector of the generator. We then evaluated the ability of the trained GANs by comparing the original dataset with the generated dataset, and generated plausible progression images of pneumonia.

Keywords

• Chest X–ray
• generative adversarial networks
• pneumonia
• transfer learning