XPGAN: X-Ray Projected Generative Adversarial Network For Improving Covid-19 Image Classification

Tran Minh Quan, Huynh Minh Thanh, Ta Duc Huy, Nguyen Do Trung Chanh, Nguyen Thi Phuong Anh, Phan Hoan Vu, Nguyen Hoang Nam, Tran Quy Tuong, Vu Minh Dien, Bui Van Giang, Bui Huu Trung, Steven Quoc Hung Truong

Abstract: This work aims to fight against the current outbreak pandemic by developing a method to classify suspected infected COVID-19 cases. Driven by the urgency, due to the vastly increased number of patients and deaths worldwide, we rely on situationally pragmatic chest X-ray scans and state-of-the-art deep learning techniques to build a robust diagnosis for massive screening, early detection, and in-time isolation decision making. The proposed solution, X-ray Projected Generative Adversarial Network (XPGAN), addresses the most fundamental issue in training such a deep neural network on limited human-annotated datasets. By leveraging the generative adversarial network, we can synthesize a large amount of chest X-ray images with prior categories from more accurate 3D Computed Tomography data, including COVID-19, and jointly train a model with a few hundreds of positive samples. As a result, XPGAN outperforms the vanilla DenseNet121 models and other competing baselines trained on the same frontal chest X-ray images.