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    Reliable COVID-19 MESHD Detection Using Chest X-ray Images

    Authors: Aysen Degerli; Mete Ahishali; Serkan Kiranyaz; Muhammad E. H. Chowdhury; Moncef Gabbouj

    id:2101.12254v1 Date: 2021-01-28 Source: arXiv

    Coronavirus disease 2019 MESHD ( COVID-19 MESHD) has emerged the need for computer-aided diagnosis with automatic, accurate, and fast algorithms. Recent studies have applied Machine Learning algorithms MESHD for COVID-19 MESHD diagnosis over chest X-ray (CXR) images. However, the data scarcity in these studies prevents a reliable evaluation with the potential of overfitting and limits the performance of deep networks. Moreover, these networks can discriminate COVID-19 MESHD pneumonia usually from healthy subjects only or occasionally, from limited pneumonia MESHD types. Thus, there is a need for a robust and accurate COVID-19 MESHD detector evaluated over a large CXR dataset. To address this need, in this study, we propose a reliable COVID-19 MESHD detection network: ReCovNet, which can discriminate COVID-19 MESHD pneumonia MESHD from 14 different thoracic diseases MESHD and healthy subjects. To accomplish this, we have compiled the largest COVID-19 MESHD CXR dataset: QaTa-COV19 with 124,616 images including 4603 COVID-19 MESHD samples. The proposed ReCovNet achieved a detection performance with 98.57% sensitivity and 99.77% specificity.

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MeSH Disease
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