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Article summary:
1. This review presents a comprehensive study on the role of generative adversarial networks (GANs) in addressing the challenges related to COVID-19 data scarcity and diagnosis.
2. The review summarizes different GAN methods and lung imaging data sets for COVID-19, exploring applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code.
3. Studies have shown that GANs have great potential to address the data scarcity challenge for lung images in COVID-19, contributing to enhancing convolutional neural network (CNN) performance through superresolution of images and segmentation.
Article analysis:
This review presents a comprehensive study on the role of generative adversarial networks (GANs) in addressing the challenges related to COVID-19 data scarcity and diagnosis. The review is conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines for systematic and scoping reviews. The search was conducted using intervention keywords such as “generative adversarial networks” and “GANs” as well as application keywords such as “COVID-19” and “coronavirus” from October 11-13, 2021 on five databases: PubMed, IEEE Xplore, Association for Computing Machinery (ACM) Digital Library, Scopus, and Google Scholar.
The article is generally trustworthy due to its use of PRISMA-ScR guidelines for systematic reviews which ensures that only reliable studies are included in the review. Furthermore, two independent reviewers were used to screen titles and abstracts with good agreement between them (Cohen κ = 0.89). However, there are some potential biases that should be noted when considering this article's trustworthiness. Firstly, only studies published from 2020 to 2022 were included which may lead to bias towards more recent studies which may not be representative of all relevant research on this topic. Secondly, only English language studies were included which may lead to bias towards studies from countries where English is widely spoken or published in English language journals/conferences which may not be representative of all relevant research on this topic. Thirdly, no restrictions were imposed on study design or outcomes which could lead to bias towards certain types of studies or results being overrepresented in this review if they are more likely to be published than others due to publication bias or