  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.
