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Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs
Portable chest X-ray (pCXR) has become an indispensable tool in the management of Coronavirus Disease 2019 (COVID-19) lung infection. This study employed deep-learning convolutional neural networks to classify COVID-19 lung infections on pCXR from normal and related lung infections to potentially en...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7649013/ https://www.ncbi.nlm.nih.gov/pubmed/33194447 http://dx.doi.org/10.7717/peerj.10309 |
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author | Kikkisetti, Shreeja Zhu, Jocelyn Shen, Beiyi Li, Haifang Duong, Tim Q. |
author_facet | Kikkisetti, Shreeja Zhu, Jocelyn Shen, Beiyi Li, Haifang Duong, Tim Q. |
author_sort | Kikkisetti, Shreeja |
collection | PubMed |
description | Portable chest X-ray (pCXR) has become an indispensable tool in the management of Coronavirus Disease 2019 (COVID-19) lung infection. This study employed deep-learning convolutional neural networks to classify COVID-19 lung infections on pCXR from normal and related lung infections to potentially enable more timely and accurate diagnosis. This retrospect study employed deep-learning convolutional neural network (CNN) with transfer learning to classify based on pCXRs COVID-19 pneumonia (N = 455) on pCXR from normal (N = 532), bacterial pneumonia (N = 492), and non-COVID viral pneumonia (N = 552). The data was randomly split into 75% training and 25% testing, randomly. A five-fold cross-validation was used for the testing set separately. Performance was evaluated using receiver-operating curve analysis. Comparison was made with CNN operated on the whole pCXR and segmented lungs. CNN accurately classified COVID-19 pCXR from those of normal, bacterial pneumonia, and non-COVID-19 viral pneumonia patients in a multiclass model. The overall sensitivity, specificity, accuracy, and AUC were 0.79, 0.93, and 0.79, 0.85 respectively (whole pCXR), and were 0.91, 0.93, 0.88, and 0.89 (CXR of segmented lung). The performance was generally better using segmented lungs. Heatmaps showed that CNN accurately localized areas of hazy appearance, ground glass opacity and/or consolidation on the pCXR. Deep-learning convolutional neural network with transfer learning accurately classifies COVID-19 on portable chest X-ray against normal, bacterial pneumonia or non-COVID viral pneumonia. This approach has the potential to help radiologists and frontline physicians by providing more timely and accurate diagnosis. |
format | Online Article Text |
id | pubmed-7649013 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-76490132020-11-12 Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs Kikkisetti, Shreeja Zhu, Jocelyn Shen, Beiyi Li, Haifang Duong, Tim Q. PeerJ Bioinformatics Portable chest X-ray (pCXR) has become an indispensable tool in the management of Coronavirus Disease 2019 (COVID-19) lung infection. This study employed deep-learning convolutional neural networks to classify COVID-19 lung infections on pCXR from normal and related lung infections to potentially enable more timely and accurate diagnosis. This retrospect study employed deep-learning convolutional neural network (CNN) with transfer learning to classify based on pCXRs COVID-19 pneumonia (N = 455) on pCXR from normal (N = 532), bacterial pneumonia (N = 492), and non-COVID viral pneumonia (N = 552). The data was randomly split into 75% training and 25% testing, randomly. A five-fold cross-validation was used for the testing set separately. Performance was evaluated using receiver-operating curve analysis. Comparison was made with CNN operated on the whole pCXR and segmented lungs. CNN accurately classified COVID-19 pCXR from those of normal, bacterial pneumonia, and non-COVID-19 viral pneumonia patients in a multiclass model. The overall sensitivity, specificity, accuracy, and AUC were 0.79, 0.93, and 0.79, 0.85 respectively (whole pCXR), and were 0.91, 0.93, 0.88, and 0.89 (CXR of segmented lung). The performance was generally better using segmented lungs. Heatmaps showed that CNN accurately localized areas of hazy appearance, ground glass opacity and/or consolidation on the pCXR. Deep-learning convolutional neural network with transfer learning accurately classifies COVID-19 on portable chest X-ray against normal, bacterial pneumonia or non-COVID viral pneumonia. This approach has the potential to help radiologists and frontline physicians by providing more timely and accurate diagnosis. PeerJ Inc. 2020-11-05 /pmc/articles/PMC7649013/ /pubmed/33194447 http://dx.doi.org/10.7717/peerj.10309 Text en © 2020 Kikkisetti et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Kikkisetti, Shreeja Zhu, Jocelyn Shen, Beiyi Li, Haifang Duong, Tim Q. Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs |
title | Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs |
title_full | Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs |
title_fullStr | Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs |
title_full_unstemmed | Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs |
title_short | Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs |
title_sort | deep-learning convolutional neural networks with transfer learning accurately classify covid-19 lung infection on portable chest radiographs |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7649013/ https://www.ncbi.nlm.nih.gov/pubmed/33194447 http://dx.doi.org/10.7717/peerj.10309 |
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