mlsanityData to 7 Oct 2026

Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning

arXiv 2004.09363 ↗First posted 20 Apr 2020cs.CVcited by 67

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Abstract (arXiv)

The COVID-19 pandemic is causing a major outbreak in more than 150 countries around the world, having a severe impact on the health and life of many people globally. One of the crucial step in fighting COVID-19 is the ability to detect the infected patients early enough, and put them under special care. Detecting this disease from radiography and radiology images is perhaps one of the fastest ways to diagnose the patients. Some of the early studies showed specific abnormalities in the chest radiograms of patients infected with COVID-19. Inspired by earlier works, we study the application of deep learning models to detect COVID-19 patients from their chest radiography images. We first prepare a dataset of 5,000 Chest X-rays from the publicly available datasets. Images exhibiting COVID-19 disease presence were identified by board-certified radiologist. Transfer learning on a subset of 2,000 radiograms was used to train four popular convolutional neural networks, including ResNet18, ResNet50, SqueezeNet, and DenseNet-121, to identify COVID-19 disease in the analyzed chest X-ray images. We evaluated these models on the remaining 3,000 images, and most of these networks achieved a sensitivity rate of 98% (± 3%), while having a specificity rate of around 90%. Besides sensitivity and specificity rates, we also present the receiver operating characteristic (ROC) curve, precision-recall curve, average prediction, and confusion matrix of each model. We also used a technique to generate heatmaps of lung regions potentially infected by COVID-19 and show that the generated heatmaps contain most of the infected areas annotated by our board certified radiologist. While the achieved performance is very encouraging, further analysis is required on a larger set of COVID-19 images, to have a more reliable estimation of accuracy rates. The dataset, model implementations (in PyTorch), and evaluations, are all made publicly available for research community at https://github.com/shervinmin/DeepCovid.git

Key facts. Cited by 67 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Apr 2020.

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Citing papers per year: 2021: 25; 2022: 22; 2023: 8; 2024: 4; 2025: 6; 2026: 2.

67 papers.

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  1. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  2. 2022Dataset Distillation for Medical Dataset Sharingcited by 25
  3. 2021COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Imagescited by 22
  4. 2021Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantificationcited by 21
  5. 2021Self-supervised deep convolutional neural network for chest X-ray classificationcited by 16
  6. 2021Parametric Scattering Networkscited by 15
  7. 2022Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approachescited by 15
  8. 2022COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNscited by 12
  9. 2021Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray imagescited by 10
  10. 2021Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptationcited by 10
  11. 2021Empirical Quantitative Analysis of COVID-19 Forecasting Modelscited by 8
  12. 2021COVID-19 Modeling: A Reviewcited by 7
38 more
  1. 2022HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretrainingcited by 7
  2. 2022COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Imagescited by 7
  3. 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
  4. 2021COVID-19 Detection in Chest X-ray Images Using Swin-Transformer and Transformer in Transformercited by 6
  5. 2022Boost Test-Time Performance with Closed-Loop Inferencecited by 6
  6. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  7. 2021DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rayscited by 5
  8. 2021Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Reviewcited by 5
  9. 2022Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBMcited by 4
  10. 2021Automated lung segmentation from CT images of normal and COVID-19 pneumonia patientscited by 3
  11. 2021Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Reviewcited by 3
  12. 2022Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBMcited by 3
  13. 2022How Well Do Vision Transformers (VTs) Transfer To The Non-Natural Image Domain? An Empirical Study Involving Art Classificationcited by 3
  14. 2022Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensemblingcited by 3
  15. 2023Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidancecited by 3
  16. 2024Domain Adaptation Using Pseudo Labels for COVID-19 Detectioncited by 3
  17. 2021A Novel Interaction-based Methodology Towards Explainable AI with Better Understanding of Pneumonia Chest X-ray Imagescited by 2
  18. 2022Deep Pneumonia: Attention-Based Contrastive Learning for Class-Imbalanced Pneumonia Lesion Recognition in Chest X-rayscited by 2
  19. 2022Deeply Supervised Layer Selective Attention Network: Towards Label-Efficient Learning for Medical Image Classificationcited by 2
  20. 2023Instrumental Variable Learning for Chest X-ray Classificationcited by 2
  21. 2021Interpretable COVID-19 Chest X-Ray Classification via Orthogonality Constraintcited by 1
  22. 2021Evolving Deep Convolutional Neural Network by Hybrid Sine-Cosine and Extreme Learning Machine for Real-time COVID19 Diagnosis from X-Ray Imagescited by 1
  23. 2021A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imagingcited by 1
  24. 2021CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Imagescited by 1
  25. 2022CNN-based Local Vision Transformer for COVID-19 Diagnosiscited by 1
  26. 2022CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosiscited by 1
  27. 2022POLCOVID: a multicenter multiclass chest X-ray database (Poland, 2020-2021)cited by 1
  28. 2024Secure Federated Learning Approaches to Diagnosing COVID-19cited by 1
  29. 2024Improving Fairness of Automated Chest X-ray Diagnosis by Contrastive Learningcited by 1
  30. 2024Survival and grade of the glioma prediction using transfer learningcited by 1
  31. 2021An Interaction-based Convolutional Neural Network (ICNN) Towards Better Understanding of COVID-19 X-ray Imagescited by 0
  32. 2021DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19cited by 0
  33. 2021A Data Augmented Approach to Transfer Learning for Covid-19 Detectioncited by 0
  34. 2021COVID-19 Pneumonia and Influenza Pneumonia Detection Using Convolutional Neural Networkscited by 0
  35. 2021A Comparative Analysis of Machine Learning Approaches for Automated Face Mask Detection During COVID-19cited by 0
  36. 2022POTHER: Patch-Voted Deep Learning-Based Chest X-ray Bias Analysis for COVID-19 Detectioncited by 0
  37. 2022A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray Images using Convolutional Neural Networkscited by 0
  38. 2022CNN-based Classification Framework for Lung Tissues with Auxiliary Informationcited by 0

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