COVID-19 Classification Using Medical Image Synthesis by Generative Adversarial Networks

The outbreak of novel coronavirus disease 2019, also called COVID-19, in Wuhan, China, began in December 2019. Since its outbreak, infectious disease has rapidly spread across the globe. The testing methods adopted by the medical practitioners gave false negatives, which is a big challenge. Medical imaging using deep learning can be adopted to speed up the testing process and avoid false negatives. This work proposes a novel approach, COVID-19 GAN, to perform coronavirus disease classification using medical image synthesis by a generative adversarial network. Detecting coronavirus infections from the chest X-ray images is very crucial for its early diagnosis and effective treatment. To boost the performance of the deep learning model and improve the accuracy of classification, synthetic data augmentation is performed using generative adversarial networks. Here, the available COVID-19 positive chest X-ray images are fed into the styleGAN2 model. The styleGAN model is trained, and the data necessary for training the deep learning model for coronavirus classification is generated. The generated COVID-19 positive chest X-ray images and the normal chest X-ray images are fed into the deep learning model for training. An accuracy of 99.78% is achieved in classifying chest X-ray images using CNN binary classifier model.

Automated summary

The COVID-19 pandemic has rapidly spread globally, posing challenges with false negatives in medical testing. This study proposes a COVID-19 GAN approach that uses generative adversarial networks for medical image synthesis, aiming to accelerate testing processes and prevent false negatives. By performing synthetic data augmentation and training a deep learning model using generated COVID-19 positive chest X-ray images, an accuracy rate of 99.78% was achieved in classifying chest X-ray images.