Reliable COVID-19 Detection Using Chest X-ray Images

• URL: http://arxiv.org/abs/2101.12254v1
• Date: Thu, 28 Jan 2021 19:57:21 GMT
• Title: Reliable COVID-19 Detection Using Chest X-ray Images
• Authors: Aysen Degerli, Mete Ahishali, Serkan Kiranyaz, Muhammad E. H. Chowdhury, Moncef Gabbouj
• Abstract summary: We have compiled the largest COVID-19 CXR dataset: QaTa-COV19 with 124,616 images including 4603 COVID-19 samples. The proposed ReCovNet achieved a detection performance with 98.57% sensitivity and 99.77% specificity.
• Score: 25.179817545627596
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Coronavirus disease 2019 (COVID-19) has emerged the need for computer-aided diagnosis with automatic, accurate, and fast algorithms. Recent studies have applied Machine Learning algorithms for COVID-19 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 pneumonia usually from healthy subjects only or occasionally, from limited pneumonia types. Thus, there is a need for a robust and accurate COVID-19 detector evaluated over a large CXR dataset. To address this need, in this study, we propose a reliable COVID-19 detection network: ReCovNet, which can discriminate COVID-19 pneumonia from 14 different thoracic diseases and healthy subjects. To accomplish this, we have compiled the largest COVID-19 CXR dataset: QaTa-COV19 with 124,616 images including 4603 COVID-19 samples. The proposed ReCovNet achieved a detection performance with 98.57% sensitivity and 99.77% specificity.

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