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MA-Net:Mutex attention network for COVID-19 diagnosis on CT images
COVID-19 is an infectious pneumonia caused by 2019-nCoV. The number of newly confirmed cases and confirmed deaths continues to remain at a high level. RT–PCR is the gold standard for the COVID-19 diagnosis, but the computed tomography (CT) imaging technique is an important auxiliary diagnostic tool....
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994185/ https://www.ncbi.nlm.nih.gov/pubmed/35431458 http://dx.doi.org/10.1007/s10489-022-03431-5 |
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author | Zheng, BingBing Zhu, Yu Shi, Qin Yang, Dawei Shao, Yanmei Xu, Tao |
author_facet | Zheng, BingBing Zhu, Yu Shi, Qin Yang, Dawei Shao, Yanmei Xu, Tao |
author_sort | Zheng, BingBing |
collection | PubMed |
description | COVID-19 is an infectious pneumonia caused by 2019-nCoV. The number of newly confirmed cases and confirmed deaths continues to remain at a high level. RT–PCR is the gold standard for the COVID-19 diagnosis, but the computed tomography (CT) imaging technique is an important auxiliary diagnostic tool. In this paper, a deep learning network mutex attention network (MA-Net) is proposed for COVID-19 auxiliary diagnosis on CT images. Using positive and negative samples as mutex inputs, the proposed network combines mutex attention block (MAB) and fusion attention block (FAB) for the diagnosis of COVID-19. MAB uses the distance between mutex inputs as a weight to make features more distinguishable for preferable diagnostic results. FAB acts to fuse features to obtain more representative features. Particularly, an adaptive weight multiloss function is proposed for better effect. The accuracy, specificity and sensitivity were reported to be as high as 98.17%, 97.25% and 98.79% on the COVID-19 dataset-A provided by the Affiliated Medical College of Qingdao University, respectively. State-of-the-art results have also been achieved on three other public COVID-19 datasets. The results show that compared with other methods, the proposed network can provide effective auxiliary information for the diagnosis of COVID-19 on CT images. |
format | Online Article Text |
id | pubmed-8994185 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-89941852022-04-11 MA-Net:Mutex attention network for COVID-19 diagnosis on CT images Zheng, BingBing Zhu, Yu Shi, Qin Yang, Dawei Shao, Yanmei Xu, Tao Appl Intell (Dordr) Article COVID-19 is an infectious pneumonia caused by 2019-nCoV. The number of newly confirmed cases and confirmed deaths continues to remain at a high level. RT–PCR is the gold standard for the COVID-19 diagnosis, but the computed tomography (CT) imaging technique is an important auxiliary diagnostic tool. In this paper, a deep learning network mutex attention network (MA-Net) is proposed for COVID-19 auxiliary diagnosis on CT images. Using positive and negative samples as mutex inputs, the proposed network combines mutex attention block (MAB) and fusion attention block (FAB) for the diagnosis of COVID-19. MAB uses the distance between mutex inputs as a weight to make features more distinguishable for preferable diagnostic results. FAB acts to fuse features to obtain more representative features. Particularly, an adaptive weight multiloss function is proposed for better effect. The accuracy, specificity and sensitivity were reported to be as high as 98.17%, 97.25% and 98.79% on the COVID-19 dataset-A provided by the Affiliated Medical College of Qingdao University, respectively. State-of-the-art results have also been achieved on three other public COVID-19 datasets. The results show that compared with other methods, the proposed network can provide effective auxiliary information for the diagnosis of COVID-19 on CT images. Springer US 2022-04-09 2022 /pmc/articles/PMC8994185/ /pubmed/35431458 http://dx.doi.org/10.1007/s10489-022-03431-5 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Zheng, BingBing Zhu, Yu Shi, Qin Yang, Dawei Shao, Yanmei Xu, Tao MA-Net:Mutex attention network for COVID-19 diagnosis on CT images |
title | MA-Net:Mutex attention network for COVID-19 diagnosis on CT images |
title_full | MA-Net:Mutex attention network for COVID-19 diagnosis on CT images |
title_fullStr | MA-Net:Mutex attention network for COVID-19 diagnosis on CT images |
title_full_unstemmed | MA-Net:Mutex attention network for COVID-19 diagnosis on CT images |
title_short | MA-Net:Mutex attention network for COVID-19 diagnosis on CT images |
title_sort | ma-net:mutex attention network for covid-19 diagnosis on ct images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994185/ https://www.ncbi.nlm.nih.gov/pubmed/35431458 http://dx.doi.org/10.1007/s10489-022-03431-5 |
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