  Coronavirus disease 2019 (COVID-19) has been diagnosed automatically using
Machine Learning algorithms over chest X-ray (CXR) images. However, most of the
earlier studies used Deep Learning models over scarce datasets bearing the risk
of overfitting. Additionally, previous studies have revealed the fact that deep
networks are not reliable for classification since their decisions may
originate from irrelevant areas on the CXRs. Therefore, in this study, we
propose Operational Segmentation Network (OSegNet) that performs detection by
segmenting COVID-19 pneumonia for a reliable diagnosis. To address the data
scarcity encountered in training and especially in evaluation, this study
extends the largest COVID-19 CXR dataset: QaTa-COV19 with 121,378 CXRs
including 9258 COVID-19 samples with their corresponding ground-truth
segmentation masks that are publicly shared with the research community.
Consequently, OSegNet has achieved a detection performance with the highest
accuracy of 99.65% among the state-of-the-art deep models with 98.09%
precision.
