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Covid-19 is a novel epidemic that has hugely impacted countries worldwide [13];and for which there is a need for quick and accurate screening methods Current testing methods include the reverse transcription-polymerase chain reaction test and medical diagnosis using computed tomography scans Both of these require expensive technologies as well as highly-trained practitioners and thus are in short supply [18] Less developed countries and overloaded hospitals have increased the demand for cheap, easy and accurate screening methods [4] X-ray devices are now cheap, portable and easy to use;there are few professionals, however, who are capable of manually identifying Covid-19 from a chest X-ray We suggest implementing a machine learning model that incorporates transfer learning to automatically detect Covid-19 from chest X-ray images The suggested model is built on top of the VGG16 architecture and pre-trained ImageNet weights Compared with the VGG19, Inception-V3, Inception-ResNet, Xception, RestNet152-V2, and DenseNet201 models, the VGG16 model achieved the highest testing accuracy of 98% on 10 epochs as well as high positive-class accuracy Gradient-weighted class activation mapping (Grad-CAM) was also applied to detect the regions that have a greater impact on the model classification decision © 2020 ACM
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