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

arXiv: https://arxiv.org/abs/2205.07236 · HTML: https://mlsanity.com/paper/2205.07236/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 86 references that are in the map.
- First posted on arXiv 15 May 2022.

## Builds on (most cited first, 17 of 17)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)
- [Can AI help in screening Viral and COVID-19 pneumonia?](https://mlsanity.com/paper/2003.13145/) (2020)
- [CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection](https://mlsanity.com/paper/2103.05094/) (2021)
- [COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network](https://mlsanity.com/paper/2007.14638/) (2020)
- [FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia](https://mlsanity.com/paper/2104.12581/) (2021)
- [Generative Adversarial Network for Medical Images (MI-GAN)](https://mlsanity.com/paper/1810.00551/) (2018)
- [Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2010.10266/) (2020)
- [RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray](https://mlsanity.com/paper/2010.06418/) (2020)
- [Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks](https://mlsanity.com/paper/2009.05752/) (2020)
- [Generating Realistic COVID19 X-rays with a Mean Teacher + Transfer Learning GAN](https://mlsanity.com/paper/2009.12478/) (2020)
- [Towards Unbiased COVID-19 Lesion Localisation and Segmentation via Weakly Supervised Learning](https://mlsanity.com/paper/2103.00780/) (2021)
- [Signal Propagation in a Gradient-Based and Evolutionary Learning System](https://mlsanity.com/paper/2102.08929/) (2021)
- [Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network](https://mlsanity.com/paper/2104.02060/) (2021)
- [Generation of COVID-19 Chest CT Scan Images using Generative Adversarial Networks](https://mlsanity.com/paper/2105.11241/) (2021)
- [A deep learning pipeline for identification of motor units in musculoskeletal ultrasound](https://mlsanity.com/paper/2010.03028/) (2020)

## Built on by (most cited first, 3 of 3)

- [Evaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagery](https://mlsanity.com/paper/2208.05593/) (2022)
- [A Trustworthy Framework for Medical Image Analysis with Deep Learning](https://mlsanity.com/paper/2212.02764/) (2022)
- [Cross Modality Medical Image Synthesis for Improving Liver Segmentation](https://mlsanity.com/paper/2503.00945/) (2025)

## Abstract (arXiv)

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. 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. Conclusion: 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.