OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images
This addresses the need for reliable automated COVID-19 diagnosis for medical applications, though it is incremental as it builds on existing segmentation methods.
The study tackled the problem of unreliable COVID-19 detection from chest X-ray images by proposing OSegNet, which segments pneumonia for diagnosis, achieving 99.65% accuracy and 98.09% precision on a new dataset.
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.