# CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection

arXiv: https://arxiv.org/abs/2103.05094 · HTML: https://mlsanity.com/paper/2103.05094/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?](https://mlsanity.com/paper/1706.00712/) (2017)
- [Medical Image Synthesis with Context-Aware Generative Adversarial Networks](https://mlsanity.com/paper/1612.05362/) (2016)
- [Exploring the Landscape of Spatial Robustness](https://mlsanity.com/paper/1712.02779/) (2017)
- [SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation](https://mlsanity.com/paper/1706.01805/) (2017)
- [AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App](https://mlsanity.com/paper/2004.01275/) (2020)
- [Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation](https://mlsanity.com/paper/1505.03046/) (2015)
- [High-resolution medical image synthesis using progressively grown generative adversarial networks](https://mlsanity.com/paper/1805.03144/) (2018)

## Built on by (most cited first, 30 of 64)

- [RoentGen: Vision-Language Foundation Model for Chest X-ray Generation](https://mlsanity.com/paper/2211.12737/) (2022)
- [Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review](https://mlsanity.com/paper/2307.13125/) (2023)
- [COVID-19 Infection Localization and Severity Grading from Chest X-ray Images](https://mlsanity.com/paper/2103.07985/) (2021)
- [A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models](https://mlsanity.com/paper/2208.01230/) (2022)
- [Manifold Topology Divergence: a Framework for Comparing Data Manifolds](https://mlsanity.com/paper/2106.04024/) (2021)
- [VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers](https://mlsanity.com/paper/2104.06757/) (2021)
- [Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder](https://mlsanity.com/paper/2105.00026/) (2021)
- [A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis](https://mlsanity.com/paper/2201.07646/) (2022)
- [FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia](https://mlsanity.com/paper/2104.12581/) (2021)
- [Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data](https://mlsanity.com/paper/2109.06486/) (2021)
- [Analyzing Tumors by Synthesis](https://mlsanity.com/paper/2409.06035/) (2024)
- [Synthetic Data in Healthcare](https://mlsanity.com/paper/2304.03243/) (2023)
- [The Prominence of Artificial Intelligence in COVID-19](https://mlsanity.com/paper/2111.09537/) (2021)
- [A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Images](https://mlsanity.com/paper/2110.06340/) (2021)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A review](https://mlsanity.com/paper/2110.14910/) (2021)
- [Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review](https://mlsanity.com/paper/2112.12705/) (2021)
- [Regularizing Neural Networks with Meta-Learning Generative Models](https://mlsanity.com/paper/2307.13899/) (2023)
- [Generative Adversarial Networks and Image-Based Malware Classification](https://mlsanity.com/paper/2207.00421/) (2022)
- [A Unified Framework for Generative Data Augmentation: A Comprehensive Survey](https://mlsanity.com/paper/2310.00277/) (2023)
- [Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review](https://mlsanity.com/paper/2205.07236/) (2022)
- [Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling](https://mlsanity.com/paper/2212.09281/) (2022)
- [Leveraging GANs for data scarcity of COVID-19: Beyond the hype](https://mlsanity.com/paper/2304.03536/) (2023)
- [CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular Data Synthesis](https://mlsanity.com/paper/2307.00384/) (2023)
- [Privacy Threats in Stable Diffusion Models](https://mlsanity.com/paper/2311.09355/) (2023)
- [Hybrid Efficient Unsupervised Anomaly Detection for Early Pandemic Case Identification](https://mlsanity.com/paper/2408.05347/) (2024)
- [Generation of COVID-19 Chest CT Scan Images using Generative Adversarial Networks](https://mlsanity.com/paper/2105.11241/) (2021)
- [Learning Stage-wise GANs for Whistle Extraction in Time-Frequency Spectrograms](https://mlsanity.com/paper/2304.02714/) (2023)
- [Performance of GAN-based augmentation for deep learning COVID-19 image classification](https://mlsanity.com/paper/2304.09067/) (2023)
- [Learning Sequential Information in Task-based fMRI for Synthetic Data Augmentation](https://mlsanity.com/paper/2308.15564/) (2023)

## Abstract (arXiv)

Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a detrimental effect on the global economy and health. A positive chest X-ray of infected patients is a crucial step in the battle against COVID-19. Early results suggest that abnormalities exist in chest X-rays of patients suggestive of COVID-19. This has led to the introduction of a variety of deep learning systems and studies have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic. Deep learning networks like convolutional neural networks (CNNs) need a substantial amount of training data. Because the outbreak is recent, it is difficult to gather a significant number of radiographic images in such a short time. Therefore, in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing an Auxiliary Classifier Generative Adversarial Network (ACGAN) based model called CovidGAN. In addition, we demonstrate that the synthetic images produced from CovidGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. By adding synthetic images produced by CovidGAN, the accuracy increased to 95%. We hope this method will speed up COVID-19 detection and lead to more robust systems of radiology.