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A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis
Till August 17, 2020, COVID-19 has caused 21.59 million confirmed cases in more than 227 countries and territories, and 26 naval ships. Chest CT is an effective way to detect COVID-19. This study proposed a novel deep learning model that can diagnose COVID-19 on chest CT more accurately and swiftly....
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7609373/ https://www.ncbi.nlm.nih.gov/pubmed/33169050 http://dx.doi.org/10.1007/s00138-020-01128-8 |
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author | Zhang, Yu-Dong Satapathy, Suresh Chandra Liu, Shuaiqi Li, Guang-Run |
author_facet | Zhang, Yu-Dong Satapathy, Suresh Chandra Liu, Shuaiqi Li, Guang-Run |
author_sort | Zhang, Yu-Dong |
collection | PubMed |
description | Till August 17, 2020, COVID-19 has caused 21.59 million confirmed cases in more than 227 countries and territories, and 26 naval ships. Chest CT is an effective way to detect COVID-19. This study proposed a novel deep learning model that can diagnose COVID-19 on chest CT more accurately and swiftly. Based on traditional deep convolutional neural network (DCNN) model, we proposed three improvements: (i) We introduced stochastic pooling to replace average pooling and max pooling; (ii) We combined conv layer with batch normalization layer and obtained the conv block (CB); (iii) We combined dropout layer with fully connected layer and obtained the fully connected block (FCB). Our algorithm achieved a sensitivity of 93.28% ± 1.50%, a specificity of 94.00% ± 1.56%, and an accuracy of 93.64% ± 1.42%, in identifying COVID-19 from normal subjects. We proved using stochastic pooling yields better performance than average pooling and max pooling. We compared different structure configurations and proved our 3CB + 2FCB yields the best performance. The proposed model is effective in detecting COVID-19 based on chest CT images. |
format | Online Article Text |
id | pubmed-7609373 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-76093732020-11-05 A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis Zhang, Yu-Dong Satapathy, Suresh Chandra Liu, Shuaiqi Li, Guang-Run Mach Vis Appl Special Issue Paper Till August 17, 2020, COVID-19 has caused 21.59 million confirmed cases in more than 227 countries and territories, and 26 naval ships. Chest CT is an effective way to detect COVID-19. This study proposed a novel deep learning model that can diagnose COVID-19 on chest CT more accurately and swiftly. Based on traditional deep convolutional neural network (DCNN) model, we proposed three improvements: (i) We introduced stochastic pooling to replace average pooling and max pooling; (ii) We combined conv layer with batch normalization layer and obtained the conv block (CB); (iii) We combined dropout layer with fully connected layer and obtained the fully connected block (FCB). Our algorithm achieved a sensitivity of 93.28% ± 1.50%, a specificity of 94.00% ± 1.56%, and an accuracy of 93.64% ± 1.42%, in identifying COVID-19 from normal subjects. We proved using stochastic pooling yields better performance than average pooling and max pooling. We compared different structure configurations and proved our 3CB + 2FCB yields the best performance. The proposed model is effective in detecting COVID-19 based on chest CT images. Springer Berlin Heidelberg 2020-11-03 2021 /pmc/articles/PMC7609373/ /pubmed/33169050 http://dx.doi.org/10.1007/s00138-020-01128-8 Text en © Springer-Verlag GmbH Germany, part of Springer Nature 2020 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 | Special Issue Paper Zhang, Yu-Dong Satapathy, Suresh Chandra Liu, Shuaiqi Li, Guang-Run A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis |
title | A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis |
title_full | A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis |
title_fullStr | A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis |
title_full_unstemmed | A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis |
title_short | A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis |
title_sort | five-layer deep convolutional neural network with stochastic pooling for chest ct-based covid-19 diagnosis |
topic | Special Issue Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7609373/ https://www.ncbi.nlm.nih.gov/pubmed/33169050 http://dx.doi.org/10.1007/s00138-020-01128-8 |
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