# Finding Covid-19 from Chest X-rays using Deep Learning on a Small Dataset

arXiv: https://arxiv.org/abs/2004.02060 · HTML: https://mlsanity.com/paper/2004.02060/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2020.

## Built on by (most cited first, 16 of 16)

- [Machine learning on small size samples: A synthetic knowledge synthesis](https://mlsanity.com/paper/2103.01002/) (2021)
- [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)
- [UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection](https://mlsanity.com/paper/2105.08590/) (2021)
- [The Prominence of Artificial Intelligence in COVID-19](https://mlsanity.com/paper/2111.09537/) (2021)
- [Performance of GAN-based augmentation for deep learning COVID-19 image classification](https://mlsanity.com/paper/2304.09067/) (2023)
- [An Evaluation of Lightweight Deep Learning Techniques in Medical Imaging for High Precision COVID-19 Diagnostics](https://mlsanity.com/paper/2305.19016/) (2023)
- [Deep Learning Models May Spuriously Classify Covid-19 from X-ray Images Based on Confounders](https://mlsanity.com/paper/2102.04300/) (2021)
- [A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging](https://mlsanity.com/paper/2108.04344/) (2021)
- [Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System](https://mlsanity.com/paper/2109.12453/) (2021)
- [Classification of COVID-19 X-ray Images Using a Combination of Deep and Handcrafted Features](https://mlsanity.com/paper/2101.07866/) (2021)
- [Explainability Guided Multi-Site COVID-19 CT Classification](https://mlsanity.com/paper/2103.13677/) (2021)
- [DPN-SENet:A self-attention mechanism neural network for detection and diagnosis of COVID-19 from chest x-ray images](https://mlsanity.com/paper/2105.09683/) (2021)
- [A Data Augmented Approach to Transfer Learning for Covid-19 Detection](https://mlsanity.com/paper/2108.02870/) (2021)
- [Interpretation of Chest x-rays affected by bullets using deep transfer learning](https://mlsanity.com/paper/2203.13461/) (2022)
- [COVID-19 Severity Classification on Chest X-ray Images](https://mlsanity.com/paper/2205.12705/) (2022)
- [Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Images](https://mlsanity.com/paper/2208.01637/) (2022)

## Abstract (arXiv)

Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We have obtained 122 chest X-rays of COVID-19 and over 4,000 chest X-rays of viral and bacterial pneumonia. A pretrained deep convolutional neural network has been tuned on 102 COVID-19 cases and 102 other pneumonia cases in a 10-fold cross validation. The results were all 102 COVID-19 cases were correctly classified and there were 8 false positives resulting in an AUC of 0.997. On a test set of 20 unseen COVID-19 cases all were correctly classified and more than 95% of 4171 other pneumonia examples were correctly classified. This study has flaws, most critically a lack of information about where in the disease process the COVID-19 cases were and the small data set size. More COVID-19 case images will enable a better answer to the question of how useful chest X-rays can be for diagnosing COVID-19 (so please send them).