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

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.

References in corpus (5)

• Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks
• COVID-19 Infection Map Generation and Detection from Chest X-Ray Images
• BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients
• PDCOVIDNet: A Parallel-Dilated Convolutional Neural Network Architecture for Detecting COVID-19 from Chest X-Ray Images
• Reliable COVID-19 Detection Using Chest X-ray Images

Cited by in corpus (3)

• SGSeg: Enabling Text-free Inference in Language-guided Segmentation of Chest X-rays via Self-guidance
• Frequency-domain Multi-modal Fusion for Language-guided Medical Image Segmentation
• R2C-GAN: Restore-to-Classify Generative Adversarial Networks for Blind X-Ray Restoration and COVID-19 Classification