Abstract
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 89.2% with a COVID-19 true positive rate of
0.8039 and an AUC of 0.95. 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 218
pneumonia cases. The overall accuracy was 91.24% with the true positive
rate for COVID-19 of 0.7879 with 6.88% false positives for a true
negative rate of 0.9312 and AUC of 0.94.
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 better answer to the question of how useful chest X-rays can be for
diagnosing COVID-19.