A bagging dynamic deep learning network for diagnosing COVID-19

Abstract COVID-19 is a serious ongoing worldwide pandemic. Using X-ray chest radiography images for automatically diagnosing COVID-19 is an effective and convenient means of providing diagnostic assistance to clinicians in practice. This paper proposes a bagging dynamic deep learning network (B-DDLN...

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Autores principales:	Zhijun Zhang, Bozhao Chen, Jiansheng Sun, Yamei Luo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ba468f8921374b3ba5e96208ea92d87c
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id	oai:doaj.org-article:ba468f8921374b3ba5e96208ea92d87c
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spelling	oai:doaj.org-article:ba468f8921374b3ba5e96208ea92d87c2021-12-02T19:06:38ZA bagging dynamic deep learning network for diagnosing COVID-1910.1038/s41598-021-95537-y2045-2322https://doaj.org/article/ba468f8921374b3ba5e96208ea92d87c2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95537-yhttps://doaj.org/toc/2045-2322Abstract COVID-19 is a serious ongoing worldwide pandemic. Using X-ray chest radiography images for automatically diagnosing COVID-19 is an effective and convenient means of providing diagnostic assistance to clinicians in practice. This paper proposes a bagging dynamic deep learning network (B-DDLN) for diagnosing COVID-19 by intelligently recognizing its symptoms in X-ray chest radiography images. After a series of preprocessing steps for images, we pre-train convolution blocks as a feature extractor. For the extracted features, a bagging dynamic learning network classifier is trained based on neural dynamic learning algorithm and bagging algorithm. B-DDLN connects the feature extractor and bagging classifier in series. Experimental results verify that the proposed B-DDLN achieves 98.8889% testing accuracy, which shows the best diagnosis performance among the existing state-of-the-art methods on the open image set. It also provides evidence for further detection and treatment.Zhijun ZhangBozhao ChenJiansheng SunYamei LuoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhijun Zhang Bozhao Chen Jiansheng Sun Yamei Luo A bagging dynamic deep learning network for diagnosing COVID-19
description	Abstract COVID-19 is a serious ongoing worldwide pandemic. Using X-ray chest radiography images for automatically diagnosing COVID-19 is an effective and convenient means of providing diagnostic assistance to clinicians in practice. This paper proposes a bagging dynamic deep learning network (B-DDLN) for diagnosing COVID-19 by intelligently recognizing its symptoms in X-ray chest radiography images. After a series of preprocessing steps for images, we pre-train convolution blocks as a feature extractor. For the extracted features, a bagging dynamic learning network classifier is trained based on neural dynamic learning algorithm and bagging algorithm. B-DDLN connects the feature extractor and bagging classifier in series. Experimental results verify that the proposed B-DDLN achieves 98.8889% testing accuracy, which shows the best diagnosis performance among the existing state-of-the-art methods on the open image set. It also provides evidence for further detection and treatment.
format	article
author	Zhijun Zhang Bozhao Chen Jiansheng Sun Yamei Luo
author_facet	Zhijun Zhang Bozhao Chen Jiansheng Sun Yamei Luo
author_sort	Zhijun Zhang
title	A bagging dynamic deep learning network for diagnosing COVID-19
title_short	A bagging dynamic deep learning network for diagnosing COVID-19
title_full	A bagging dynamic deep learning network for diagnosing COVID-19
title_fullStr	A bagging dynamic deep learning network for diagnosing COVID-19
title_full_unstemmed	A bagging dynamic deep learning network for diagnosing COVID-19
title_sort	bagging dynamic deep learning network for diagnosing covid-19
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ba468f8921374b3ba5e96208ea92d87c
work_keys_str_mv	AT zhijunzhang abaggingdynamicdeeplearningnetworkfordiagnosingcovid19 AT bozhaochen abaggingdynamicdeeplearningnetworkfordiagnosingcovid19 AT jianshengsun abaggingdynamicdeeplearningnetworkfordiagnosingcovid19 AT yameiluo abaggingdynamicdeeplearningnetworkfordiagnosingcovid19 AT zhijunzhang baggingdynamicdeeplearningnetworkfordiagnosingcovid19 AT bozhaochen baggingdynamicdeeplearningnetworkfordiagnosingcovid19 AT jianshengsun baggingdynamicdeeplearningnetworkfordiagnosingcovid19 AT yameiluo baggingdynamicdeeplearningnetworkfordiagnosingcovid19
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A bagging dynamic deep learning network for diagnosing COVID-19

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