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

arXiv: https://arxiv.org/abs/2004.09363 · HTML: https://mlsanity.com/paper/2004.09363/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2020.

## Built on by (most cited first, 30 of 67)

- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [Dataset Distillation for Medical Dataset Sharing](https://mlsanity.com/paper/2209.14603/) (2022)
- [COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Images](https://mlsanity.com/paper/2105.06640/) (2021)
- [Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantification](https://mlsanity.com/paper/2104.07235/) (2021)
- [Self-supervised deep convolutional neural network for chest X-ray classification](https://mlsanity.com/paper/2103.03055/) (2021)
- [Parametric Scattering Networks](https://mlsanity.com/paper/2107.09539/) (2021)
- [Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches](https://mlsanity.com/paper/2206.08016/) (2022)
- [COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNs](https://mlsanity.com/paper/2209.10963/) (2022)
- [Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images](https://mlsanity.com/paper/2102.06883/) (2021)
- [Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation](https://mlsanity.com/paper/2109.03478/) (2021)
- [Empirical Quantitative Analysis of COVID-19 Forecasting Models](https://mlsanity.com/paper/2110.00174/) (2021)
- [COVID-19 Modeling: A Review](https://mlsanity.com/paper/2104.12556/) (2021)
- [Boost Test-Time Performance with Closed-Loop Inference](https://mlsanity.com/paper/2203.10853/) (2022)
- [HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretraining](https://mlsanity.com/paper/2210.04477/) (2022)
- [COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Images](https://mlsanity.com/paper/2212.09276/) (2022)
- [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)
- [COVID-19 Detection in Chest X-ray Images Using Swin-Transformer and Transformer in Transformer](https://mlsanity.com/paper/2110.08427/) (2021)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays](https://mlsanity.com/paper/2104.04006/) (2021)
- [Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review](https://mlsanity.com/paper/2112.12705/) (2021)
- [Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM](https://mlsanity.com/paper/2203.14275/) (2022)
- [Automated lung segmentation from CT images of normal and COVID-19 pneumonia patients](https://mlsanity.com/paper/2104.02042/) (2021)
- [Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review](https://mlsanity.com/paper/2107.09602/) (2021)
- [Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM](https://mlsanity.com/paper/2206.04548/) (2022)
- [How Well Do Vision Transformers (VTs) Transfer To The Non-Natural Image Domain? An Empirical Study Involving Art Classification](https://mlsanity.com/paper/2208.04693/) (2022)
- [Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling](https://mlsanity.com/paper/2212.09281/) (2022)
- [Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance](https://mlsanity.com/paper/2306.01232/) (2023)
- [Domain Adaptation Using Pseudo Labels for COVID-19 Detection](https://mlsanity.com/paper/2403.11498/) (2024)
- [A Novel Interaction-based Methodology Towards Explainable AI with Better Understanding of Pneumonia Chest X-ray Images](https://mlsanity.com/paper/2104.12672/) (2021)
- [Deep Pneumonia: Attention-Based Contrastive Learning for Class-Imbalanced Pneumonia Lesion Recognition in Chest X-rays](https://mlsanity.com/paper/2207.11393/) (2022)

## 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