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 122 chest X-rays of COVID-19 and over 4,000 chest X-rays
of viral and bacterial pneumonia. A pre-trained 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 4,171
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).