# Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach

arXiv: https://arxiv.org/abs/2004.10641 · HTML: https://mlsanity.com/paper/2004.10641/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2020.

## Built on by (most cited first, 19 of 19)

- [Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge](https://mlsanity.com/paper/2101.07511/) (2021)
- [The Prominence of Artificial Intelligence in COVID-19](https://mlsanity.com/paper/2111.09537/) (2021)
- [Machine learning applications for COVID-19: A state-of-the-art review](https://mlsanity.com/paper/2101.07824/) (2021)
- [Deep Learning Models May Spuriously Classify Covid-19 from X-ray Images Based on Confounders](https://mlsanity.com/paper/2102.04300/) (2021)
- [Evolving Deep Convolutional Neural Network by Hybrid Sine-Cosine and Extreme Learning Machine for Real-time COVID19 Diagnosis from X-Ray Images](https://mlsanity.com/paper/2105.14192/) (2021)
- [Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis](https://mlsanity.com/paper/2106.09759/) (2021)
- [A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging](https://mlsanity.com/paper/2108.04344/) (2021)
- [Unlocking the Potential of Medical Imaging with ChatGPT's Intelligent Diagnostics](https://mlsanity.com/paper/2305.07429/) (2023)
- [COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classification](https://mlsanity.com/paper/2312.12876/) (2023)
- [Tiled sparse coding in eigenspaces for the COVID-19 diagnosis in chest X-ray images](https://mlsanity.com/paper/2106.14724/) (2021)
- [Detecting COVID-19 from Chest Computed Tomography Scans using AI-Driven Android Application](https://mlsanity.com/paper/2111.06254/) (2021)
- [Deep transfer learning for detecting Covid-19, Pneumonia and Tuberculosis using CXR images -- A Review](https://mlsanity.com/paper/2303.16754/) (2023)
- [A Novel Deep Learning based Model for Erythrocytes Classification and Quantification in Sickle Cell Disease](https://mlsanity.com/paper/2305.01663/) (2023)
- [Robust and Interpretable COVID-19 Diagnosis on Chest X-ray Images using Adversarial Training](https://mlsanity.com/paper/2311.14227/) (2023)
- [Symptom-based Machine Learning Models for the Early Detection of COVID-19: A Narrative Review](https://mlsanity.com/paper/2312.06832/) (2023)
- [From Lab to Pocket: A Novel Continual Learning-based Mobile Application for Screening COVID-19](https://mlsanity.com/paper/2410.12589/) (2024)
- [TagGAN: A Generative Model for Data Tagging](https://mlsanity.com/paper/2502.17836/) (2025)
- [An Integrated Deep Learning Framework Leveraging NASNet and Vision Transformer with MixProcessing for Accurate and Precise Diagnosis of Lung Diseases](https://mlsanity.com/paper/2502.20570/) (2025)
- [A Hybrid Federated Learning Based Ensemble Approach for Lung Disease Diagnosis Leveraging Fusion of SWIN Transformer and CNN](https://mlsanity.com/paper/2602.17566/) (2026)

## Abstract (arXiv)

The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. The most common symptoms of COVID-19 are dry cough, sore throat, and fever. Symptoms can progress to a severe form of pneumonia with critical complications, including septic shock, pulmonary edema, acute respiratory distress syndrome and multi-organ failure. While medical imaging is not currently recommended in Canada for primary diagnosis of COVID-19, computer-aided diagnosis systems could assist in the early detection of COVID-19 abnormalities and help to monitor the progression of the disease, potentially reduce mortality rates. In this study, we compare popular deep learning-based feature extraction frameworks for automatic COVID-19 classification. To obtain the most accurate feature, which is an essential component of learning, MobileNet, DenseNet, Xception, ResNet, InceptionV3, InceptionResNetV2, VGGNet, NASNet were chosen amongst a pool of deep convolutional neural networks. The extracted features were then fed into several machine learning classifiers to classify subjects as either a case of COVID-19 or a control. This approach avoided task-specific data pre-processing methods to support a better generalization ability for unseen data. The performance of the proposed method was validated on a publicly available COVID-19 dataset of chest X-ray and CT images. The DenseNet121 feature extractor with Bagging tree classifier achieved the best performance with 99% classification accuracy. The second-best learner was a hybrid of the a ResNet50 feature extractor trained by LightGBM with an accuracy of 98%.