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A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron

arXiv 2202.06372 ↗First posted 13 Feb 2022eess.IV cs.CVcited by 13

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Abstract (arXiv)

The Coronavirus (COVID-19) outbreak in December 2019 has become an ongoing threat to humans worldwide, creating a health crisis that infected millions of lives, as well as devastating the global economy. Deep learning (DL) techniques have proved helpful in analysis and delineation of infectious regions in radiological images in a timely manner. This paper makes an in-depth survey of DL techniques and draws a taxonomy based on diagnostic strategies and learning approaches. DL techniques are systematically categorized into classification, segmentation, and multi-stage approaches for COVID-19 diagnosis at image and region level analysis. Each category includes pre-trained and custom-made Convolutional Neural Network architectures for detecting COVID-19 infection in radiographic imaging modalities; X-Ray, and Computer Tomography (CT). Furthermore, a discussion is made on challenges in developing diagnostic techniques such as cross-platform interoperability and examining imaging modality. Similarly, a review of the various methodologies and performance measures used in these techniques is also presented. This survey provides an insight into the promising areas of research in DL for analyzing radiographic images, and further accelerates the research in designing customized DL based diagnostic tools for effectively dealing with new variants of COVID-19 and emerging challenges.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 37 of its 182 references that are in the map. First posted on arXiv 13 Feb 2022.

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37 of 182 references.

References · 37

  1. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
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25 more
  1. 2020COVID-CT-Dataset: A CT Scan Dataset about COVID-19cited by 93
  2. 2020Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networkscited by 92
  3. 2020Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networkscited by 86
  4. 2021Deep Learning for Chest X-ray Analysis: A Surveycited by 84
  5. 2020BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patientscited by 81
  6. 2020CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray imagescited by 68
  7. 2020Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19cited by 62
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 4; 2024: 2; 2025: 2; 2026: 1.

13 papers.

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  1. 2022COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNscited by 12
  2. 2023MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentationcited by 8
  3. 2022Brain Tumor MRI Classification using a Novel Deep Residual and Regional CNNcited by 7
  4. 2022A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detectioncited by 6
  5. 2023COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Imagescited by 4
  6. 2025A Novel Channel Boosted Residual CNN-Transformer with Regional-Boundary Learning for Breast Cancer Detectioncited by 3
  7. 2025CE-RS-SBCIT A Novel Channel Enhanced Hybrid CNN Transformer with Residual, Spatial, and Boundary-Aware Learning for Brain Tumor MRI Analysiscited by 3
  8. 2024A Novel Feature Map Enhancement Technique Integrating Residual CNN and Transformer for Alzheimer Diseases Diagnosiscited by 2
  9. 2022Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Frameworkcited by 1
  10. 2023Deep Neural Networks based Meta-Learning for Network Intrusion Detectioncited by 1
  11. 2023AutoLungDx: A Hybrid Deep Learning Approach for Early Lung Cancer Diagnosis Using 3D Res-U-Net, YOLOv5, and Vision Transformerscited by 1
  12. 2026RSwinV2-MD: An Enhanced Residual SwinV2 Transformer for Monkeypox Detection from Skin Imagescited by 1
1 more
  1. 2024From Lab to Pocket: A Novel Continual Learning-based Mobile Application for Screening COVID-19cited by 0