ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans

Automated diagnostic techniques based on computed tomography (CT) scans of the chest for the coronavirus disease (COVID-19) help physicians detect suspected cases rapidly and precisely, which is critical in providing timely medical treatment and preventing the spread of epidemic outbreaks. Existing capsule networks have played a significant role in automatic COVID-19 detection systems based on small datasets. However, extracting key slices is difficult because CT scans typically show many scattered lesion sections. In addition, existing max pooling sampling methods cannot effectively fuse the features from multiple regions. Therefore, in this study, we propose an attention capsule sampling network (ACSN) to detect COVID-19 based on chest CT scans. A key slices enhancement method is used to obtain critical information from a large number of slices by applying attention enhancement to key slices. Then, the lost active and background features are retained by integrating two types of sampling. The results of experiments on an open dataset of 35,000 slices show that the proposed ACSN achieve high performance compared with state-of-the-art models and exhibits 96.3% accuracy, 98.8% sensitivity, 93.8% specificity, and 98.3% area under the receiver operating characteristic curve.

Automated summary

Automated diagnostic techniques based on CT scans of the chest have been proposed to detect COVID-19 cases quickly and accurately. Existing capsule networks face challenges in extracting key slices and fusing features from multiple regions. In this study, an attention capsule sampling network (ACSN) is proposed, which achieves high performance with 96.3% accuracy, 98.8% sensitivity, 93.8% specificity, and 98.3% area under the ROC curve on a dataset of 35,000 slices.