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COVID-19 Screening Using Residual Attention Network an Artificial Intelligence Approach

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Document pages: 8 pages

Abstract: Coronavirus Disease 2019 (COVID-19) is caused by severe acute respiratorysyndrome coronavirus 2 virus (SARS-CoV-2). The virus transmits rapidly; it hasa basic reproductive number R of 2.2-2.7. In March 2020, the World HealthOrganization declared the COVID-19 outbreak a pandemic. COVID-19 is currentlyaffecting more than 200 countries with 6M active cases. An effective testingstrategy for COVID-19 is crucial to controlling the outbreak but the demand fortesting surpasses the availability of test kits that use Reverse TranscriptionPolymerase Chain Reaction (RT-PCR). In this paper, we present a technique toscreen for COVID-19 using artificial intelligence. Our technique takes onlyseconds to screen for the presence of the virus in a patient. We collected adataset of chest X-ray images and trained several popular deep convolutionneural network-based models (VGG, MobileNet, Xception, DenseNet,InceptionResNet) to classify the chest X-rays. Unsatisfied with these models,we then designed and built a Residual Attention Network that was able to screenCOVID-19 with a testing accuracy of 98 and a validation accuracy of 100 . Afeature maps visual of our model show areas in a chest X-ray which areimportant for classification. Our work can help to increase the adaptation ofAI-assisted applications in clinical practice. The code and dataset used inthis project are available atthis https URL.

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