mlsanityData to 7 Oct 2026

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

arXiv 2004.02060 ↗First posted 5 Apr 2020eess.IV cs.CV cs.LGcited by 16

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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).

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Apr 2020.

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Citing papers per year: 2021: 11; 2022: 3; 2023: 2.

16 papers.

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  1. 2021Machine learning on small size samples: A synthetic knowledge synthesiscited by 11
  2. 2021Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray imagescited by 10
  3. 2021UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detectioncited by 8
  4. 2021The Prominence of Artificial Intelligence in COVID-19cited by 7
  5. 2023Performance of GAN-based augmentation for deep learning COVID-19 image classificationcited by 2
  6. 2023An Evaluation of Lightweight Deep Learning Techniques in Medical Imaging for High Precision COVID-19 Diagnosticscited by 2
  7. 2021Deep Learning Models May Spuriously Classify Covid-19 from X-ray Images Based on Confounderscited by 1
  8. 2021A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imagingcited by 1
  9. 2021Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the Systemcited by 1
  10. 2021Classification of COVID-19 X-ray Images Using a Combination of Deep and Handcrafted Featurescited by 0
  11. 2021Explainability Guided Multi-Site COVID-19 CT Classificationcited by 0
  12. 2021DPN-SENet:A self-attention mechanism neural network for detection and diagnosis of COVID-19 from chest x-ray imagescited by 0
4 more
  1. 2021A Data Augmented Approach to Transfer Learning for Covid-19 Detectioncited by 0
  2. 2022Interpretation of Chest x-rays affected by bullets using deep transfer learningcited by 0
  3. 2022COVID-19 Severity Classification on Chest X-ray Imagescited by 0
  4. 2022Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Imagescited by 0