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The pandemic of novel Coronavirus Disease 2019 (COVID-19) is widespread allover the world causing serious health problems as well as serious impact on theglobal economy. Reliable and fast testing of the COVID-19 has been a challengefor researchers and healthcare practitioners. In this work we present a novelmachine learning (ML) integrated X-ray device in Healthcare Cyber-PhysicalSystem (H-CPS) or smart healthcare framework (called CoviLearn) to allowhealthcare practitioners to perform automatic initial screening of COVID-19patients. We propose convolutional neural network (CNN) models of X-ray imagesintegrated into an X-ray device for automatic COVID-19 detection. The proposedCoviLearn device will be useful in detecting if a person is COVID-19 positiveor negative by considering the chest X-ray image of individuals. CoviLearn willbe useful tool doctors to detect potential COVID-19 infections instantaneouslywithout taking more intrusive healthcare data samples, such as saliva andblood. COVID-19 attacks the endothelium tissues that support respiratory tract,X-rays images can be used to analyze the health of a patient lungs. As allhealthcare centers have X-ray machines, it could be possible to use proposedCoviLearn X-rays to test for COVID-19 without the especial test kits. Ourproposed automated analysis system CoviLearn which has 99% accuracy will beable to save valuable time of medical professionals as the X-ray machines comewith a drawback as it needed a radiology expert.

CoviLearn: A Machine Learning Integrated Smart X-Ray Device in Healthcare Cyber-Physical System for Automatic Initial Screening of COVID-19