摘要：Beginning in December 2019, the spread of the novel Coronavirus (COVID-19) has exposedweaknesses in healthcare systems across the world. To sufficiently contain the virus, countrieshave had to carry out a set of extraordinary measures, including exhaustive testing and screening for positive cases of the disease.It is crucial to detect and isolate those who areinfected as soon as possible to keep the virus contained.However, in countries and areaswhere there are limited COVID-19 testing kits, there is an urgent need for alternative diagnostic measures. The standard screening method currently used for detecting COVID-19cases is RT-PCR testing, which is a very time-consuming,laborious, and complicated manualprocess.Given that nearly all hospitals have x-ray imaging machines, it is possible to use X-rays to screen for COVID-19 without the dedicated test kits and separate those who areinfected and those who are not.In this study, we applied deep convolutional neural networkson chest X-rays to determine this phenomena.The proposed deep learning model produced anaverage classification accuracy of 90.64% and F1-Score of 89.8% after performing 5-foldcross-validation on a multi-class dataset consisting of COVID-19, Viral Pneumonia, andnormal X-ray images.
