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COVID-CXNet Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning

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Document pages: 21 pages

Abstract: One of the primary clinical observations for screening the infectious by thenovel coronavirus is capturing a chest x-ray image. In most of the patients, achest x-ray contains abnormalities, such as consolidation, which are theresults of COVID-19 viral pneumonia. In this study, research is conducted onefficiently detecting imaging features of this type of pneumonia using deepconvolutional neural networks in a large dataset. It is demonstrated thatsimple models, alongside the majority of pretrained networks in the literature,focus on irrelevant features for decision-making. In this paper, numerous chestx-ray images from various sources are collected, and the largest publiclyaccessible dataset is prepared. Finally, using the transfer learning paradigm,the well-known CheXNet model is utilized for developing COVID-CXNet. Thispowerful model is capable of detecting the novel coronavirus pneumonia based onrelevant and meaningful features with precise localization. COVID-CXNet is astep towards a fully automated and robust COVID-19 detection system.

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