Project Summary

Beginning in December 2019, the spread of the novel Coronavirus (COVID-19) has exposed weaknesses in healthcare systems across the world. To sufficiently contain the virus, countries have 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 are infected as soon as possible to keep the virus contained. However, in countries and areas where 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-19 cases is RT-PCR testing, which is a very time-consuming, laborious, and complicated manual process. 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 are infected and those who are not. In this study, we applied deep convolutional neural networks on chest X-rays to determine this phenomena. The proposed deep learning model produced an average classification accuracy of 90.64% and F1-Score of 89.8% after performing 5-fold cross-validation on a multi-class dataset consisting of COVID-19, Viral Pneumonia, and normal X-ray images.

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