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

arXiv 2103.05094 ↗First posted 8 Mar 2021eess.IV cs.CV cs.LGcited by 64

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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.

Key facts. Cited by 64 papers in the map (data through 7 Oct 2026). Builds on 13 of its 31 references that are in the map. First posted on arXiv 8 Mar 2021.

Builds on

13 of 31 references.

References · 13

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  3. 2014Conditional Generative Adversarial Netscited by 2,152
  4. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  5. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
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  7. 2017Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?cited by 220
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  10. 2017SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentationcited by 49
  11. 2020AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an Appcited by 48
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1 more
  1. 2018High-resolution medical image synthesis using progressively grown generative adversarial networkscited by 17

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 14; 2023: 17; 2024: 10; 2025: 6; 2026: 3.

64 papers.

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  1. 2022RoentGen: Vision-Language Foundation Model for Chest X-ray Generationcited by 89
  2. 2023Deep Learning Approaches for Data Augmentation in Medical Imaging: A Reviewcited by 48
  3. 2021COVID-19 Infection Localization and Severity Grading from Chest X-ray Imagescited by 46
  4. 2022A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Modelscited by 43
  5. 2021Manifold Topology Divergence: a Framework for Comparing Data Manifoldscited by 31
  6. 2021VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformerscited by 28
  7. 2021Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencodercited by 27
  8. 2022A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysiscited by 24
  9. 2021FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumoniacited by 22
  10. 2021Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Datacited by 18
  11. 2024Analyzing Tumors by Synthesiscited by 11
  12. 2023Synthetic Data in Healthcarecited by 8
38 more
  1. 2021The Prominence of Artificial Intelligence in COVID-19cited by 7
  2. 2021A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Imagescited by 6
  3. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  4. 2021Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A reviewcited by 5
  5. 2021Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Reviewcited by 5
  6. 2023Regularizing Neural Networks with Meta-Learning Generative Modelscited by 5
  7. 2022Generative Adversarial Networks and Image-Based Malware Classificationcited by 4
  8. 2023A Unified Framework for Generative Data Augmentation: A Comprehensive Surveycited by 4
  9. 2022Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Reviewcited by 3
  10. 2022Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensemblingcited by 3
  11. 2023Leveraging GANs for data scarcity of COVID-19: Beyond the hypecited by 3
  12. 2023CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular Data Synthesiscited by 3
  13. 2023Privacy Threats in Stable Diffusion Modelscited by 3
  14. 2024Hybrid Efficient Unsupervised Anomaly Detection for Early Pandemic Case Identificationcited by 3
  15. 2021Generation of COVID-19 Chest CT Scan Images using Generative Adversarial Networkscited by 2
  16. 2023Learning Stage-wise GANs for Whistle Extraction in Time-Frequency Spectrogramscited by 2
  17. 2023Performance of GAN-based augmentation for deep learning COVID-19 image classificationcited by 2
  18. 2023Learning Sequential Information in Task-based fMRI for Synthetic Data Augmentationcited by 2
  19. 2024MultiFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for Chest X-Ray Image Classificationcited by 2
  20. 2021A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imagingcited by 1
  21. 2022Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networkscited by 1
  22. 2022ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19 Segmentationcited by 1
  23. 2022Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognitioncited by 1
  24. 2022A novel adversarial learning strategy for medical image classificationcited by 1
  25. 2022Deterioration Prediction using Time-Series of Three Vital Signs and Current Clinical Features Amongst COVID-19 Patientscited by 1
  26. 2023Method for Generating Synthetic Data Combining Chest Radiography Images with Tabular Clinical Information Using Dual Generative Modelscited by 1
  27. 2023Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Imagescited by 1
  28. 2023COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classificationcited by 1
  29. 2024CSI4Free: GAN-Augmented mmWave CSI for Improved Pose Classificationcited by 1
  30. 2024Robustness Testing of Black-Box Models Against CT Degradation Through Test-Time Augmentationcited by 1
  31. 2024WasteGAN: Data Augmentation for Robotic Waste Sorting through Generative Adversarial Networkscited by 1
  32. 2024Generative Modeling and Data Augmentation for Power System Production Simulationcited by 1
  33. 2025Cross Modality Medical Image Synthesis for Improving Liver Segmentationcited by 1
  34. 2025Synthetic Generation and Latent Projection Denoising of Rim Lesions in Multiple Sclerosiscited by 1
  35. 2021COVID-Rate: An Automated Framework for Segmentation of COVID-19 Lesions from Chest CT Scanscited by 0
  36. 2021A Data Augmented Approach to Transfer Learning for Covid-19 Detectioncited by 0
  37. 2022Intercategorical Label Interpolation for Emotional Face Generation with Conditional Generative Adversarial Networkscited by 0
  38. 2022Machine learning approaches for COVID-19 detection from chest X-ray imaging: A Systematic Reviewcited by 0

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