L. Hall, Rahul Paul, D. Goldgof, G. Goldgof
ArXiv, 2020
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 135 chest X-rays of COVID-19 and 320 chest X-rays of viral and bacterial pneumonia. A pre-trained deep convolutional neural network, Resnet50 was tuned on 102 COVID-19 cases and 102 other pneumonia cases in a 10-fold cross validation. The results were an overall accuracy of 90.7% with a COVID-19 true positive rate of 0.83 and an AUC of 0.987, Pre-trained Resnet50 and VGG16 plus our own small CNN were tuned or trained on a balanced set of COVID-19 and pneumonia chest X-rays. An ensemble of the three types of CNN classifiers was applied to a test set of 33 unseen COVID-19 and 208 pneumonia cases. The overall accuracy was 94.4% with the true positive rate for COVID-19 of 0.969 with 6% false positives for a true negative rate of 0.94 and AUC of 0.99. This preliminary 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 at good resolution will enable a
Cited by 67 publications.
Field of study: Engineering