Two-Stage Deep Learning Framework for Discrimination between COVID-19 and Community-Acquired Pneumonia from Chest CT scans

Abstract

COVID-19 stay threatening the health infrastructure worldwide. Computed tomography (CT) was demonstrated as an informative tool for the recognition, quantification, and diagnosis of this kind of disease. It is urgent to design efficient deep learning (DL) approach to automatically localize and discriminate COVID-19 from other comparable pneumonia on lung CT scans. Thus, this study introduces a novel two-stage DL framework for discriminating COVID-19 from community-acquired pneumonia (CAP) depending on the detected infection region within CT slices. Firstly, a novel U-shaped network is presented to segment the lung area where the infection appears. Then, the concept of transfer learning is applied to the feature extraction network to empower the network capabilities in learning the disease patterns. After that, multi-scale information is captured and pooled via an attention mechanism for powerful classification performance. Thirdly, we propose an infection prediction module that use the infection location to guide the classification decision and hence provides interpretable classification decision. Finally, the proposed model was evaluated on public datasets and achieved great segmentation and classification performance outperforming the cutting-edge studies.