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

doi:10.1016/j.compbiomed.2022.106338

. 2023 Feb;153:106338.

doi: 10.1016/j.compbiomed.2022.106338. Epub 2022 Nov 22.

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

Cuihong Wen¹, Shaowu Liu², Shuai Liu³, Ali Asghar Heidari⁴, Mohammad Hijji⁵, Carmen Zarco⁶, Khan Muhammad⁷

Affiliations

¹ College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China; State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing, 100871, China.
² College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China.
³ College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China; School of Educational Science, Hunan Normal University, Changsha, 410081, China; Key Laboratory of Big Data Research and Application for Basic Education, Hunan Normal University, Changsha, 410081, China. Electronic address: liushuai@hunnu.edu.cn.
⁴ School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, 1439957131, Iran. Electronic address: as_heidari@ut.ac.ir.
⁵ Faculty of Computers and Information Technology (FCIT), University of Tabuk, Tabuk, 47711, Saudi Arabia.
⁶ Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada (UGR), Spain.
⁷ Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied AI, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, 03063, South Korea. Electronic address: khan.muhammad@ieee.org.

PMID: 36640529
PMCID: PMC9678829
DOI: 10.1016/j.compbiomed.2022.106338

Free PMC article

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

Cuihong Wen et al. Comput Biol Med. 2023 Feb.

Free PMC article

. 2023 Feb;153:106338.

doi: 10.1016/j.compbiomed.2022.106338. Epub 2022 Nov 22.

Authors

Cuihong Wen¹, Shaowu Liu², Shuai Liu³, Ali Asghar Heidari⁴, Mohammad Hijji⁵, Carmen Zarco⁶, Khan Muhammad⁷

Affiliations

¹ College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China; State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing, 100871, China.
² College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China.
³ College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China; School of Educational Science, Hunan Normal University, Changsha, 410081, China; Key Laboratory of Big Data Research and Application for Basic Education, Hunan Normal University, Changsha, 410081, China. Electronic address: liushuai@hunnu.edu.cn.
⁴ School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, 1439957131, Iran. Electronic address: as_heidari@ut.ac.ir.
⁵ Faculty of Computers and Information Technology (FCIT), University of Tabuk, Tabuk, 47711, Saudi Arabia.
⁶ Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada (UGR), Spain.
⁷ Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied AI, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, 03063, South Korea. Electronic address: khan.muhammad@ieee.org.

PMID: 36640529
PMCID: PMC9678829
DOI: 10.1016/j.compbiomed.2022.106338

Abstract

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.

Keywords: COVID-19 recognition; Capsule network; Chest CT scan; Deep learning; Feature sampling; Lung infections.

Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.