mlsanityData to 7 Oct 2026

Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning

arXiv 2104.11949 ↗First posted 24 Apr 2021eess.IV cs.CV cs.LGcited by 7

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Abstract (arXiv)

The outbreak of the corona virus disease (COVID-19) has changed the lives of most people on Earth. Given the high prevalence of this disease, its correct diagnosis in order to quarantine patients is of the utmost importance in steps of fighting this pandemic. Among the various modalities used for diagnosis, medical imaging, especially computed tomography (CT) imaging, has been the focus of many previous studies due to its accuracy and availability. In addition, automation of diagnostic methods can be of great help to physicians. In this paper, a method based on pre-trained deep neural networks is presented, which, by taking advantage of a cyclic generative adversarial net (CycleGAN) model for data augmentation, has reached state-of-the-art performance for the task at hand, i.e., 99.60% accuracy. Also, in order to evaluate the method, a dataset containing 3163 images from 189 patients has been collected and labeled by physicians. Unlike prior datasets, normal data have been collected from people suspected of having COVID-19 disease and not from data from other diseases, and this database is made available publicly.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 27 of its 105 references that are in the map. First posted on arXiv 24 Apr 2021.

Builds on

27 of 105 references.

References · 27

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15 more
  1. 2020Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19cited by 62
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Cited by year

Citing papers per year: 2021: 3; 2022: 2; 2023: 1; 2024: 0; 2025: 1.

7 papers.

  1. 2021Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Reviewcited by 10
  2. 2022Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A reviewcited by 8
  3. 2021Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regressioncited by 1
  4. 2021CCS-GAN: COVID-19 CT-scan classification with very few positive training imagescited by 1
  5. 2022Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Reviewcited by 0
  6. 2023Enhancing COVID-19 Diagnosis through Vision Transformer-Based Analysis of Chest X-ray Imagescited by 0
  7. 2025A Novel Multi-branch ConvNeXt Architecture for Identifying Subtle Pathological Features in CT Scanscited by 0