OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images

TL;DR

OSegNet is a segmentation-based deep learning model designed for COVID-19 detection in chest X-ray images, achieving high accuracy and addressing data scarcity and reliability issues present in prior methods.

Contribution

The paper introduces OSegNet, a novel segmentation network for COVID-19 detection, and extends the largest COVID-19 CXR dataset with ground-truth masks for improved training and evaluation.

Findings

Achieved 99.65% detection accuracy

Extended the QaTa-COV19 dataset with 121,378 images

Provided publicly available ground-truth segmentation masks

Abstract

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.