Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review.

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Abstract

Research on the diagnosis of COVID-19 using lungs images was limited by the scarcity of images data. Generative Adversarial Networks (GANs) are popular for synthesis and data augmentation. GANs have been explored for data augmentation to enhance the performance of Artificial Intelligence methods for the diagnosis of COVID-19 within lungs CT and X-Ray images. However, the role of GANs to overcome data scarcity for COVID-19 is not well understood.This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes the different GANs methods and the lungs images datasets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code.A search was conducted on five databases, namely Pubmed, IEEEXplore, ACM Digital Library, Scopus, and Google Scholar. The search was conducted between 11 October to 13 October 2021. The search was conducted using intervention keywords such as generative adversarial networks or GANs and application keywords such as COVID-19 and coronavirus. The review was performed following the guidelines of PRISMA-ScR for systematic and scoping reviews. Only those studies were included that reported GANs based methods for chest X-ray images, chest CT images, and chest ultrasound images. Any studies that used deep learning methods but did not use GANs were excluded. No restrictions were imposed on the country of publication, study design, or outcomes. Only those studies that were in English and were published from 2020 to 2022 were included. No studies before 2020 were included.This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lungs images data. Most of the studies (n=42) used GANs for data augmentation to enhance the performance of AI techniques for COVID-19 diagnosis. Other popular applications of GANs were segmentation of lungs and super-resolution of the lungs images. The cycleGAN and the conditional GAN were the most commonly used architectures used in nine studies each. 29 studies used chest X-Ray images while 21 studies used CT images for the training of GANs. For majority of the studies (n=47), the experiments were done and results were reported using publicly available data. A secondary evaluation of the results by radiologists/clinicians was reported by only two studies.Studies have shown that GANs have great potential to address the data scarcity challenge for lungs images of COVID-19. Data synthesized with GANs have been helpful to improve the training of the Convolutional Neural Network (CNN) models trained for the diagnosis of COVID-19. Besides, GANs have also contributed to enhancing the CNNs performance through the super-resolution of the images and segmentation. This review also identified key limitations of the potential transformation of GANs based methods in clinical applications.