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Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection
BACKGROUND: The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7686836/ https://www.ncbi.nlm.nih.gov/pubmed/33239006 http://dx.doi.org/10.1186/s12938-020-00831-x |
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author | Hussain, Lal Nguyen, Tony Li, Haifang Abbasi, Adeel A. Lone, Kashif J. Zhao, Zirun Zaib, Mahnoor Chen, Anne Duong, Tim Q. |
author_facet | Hussain, Lal Nguyen, Tony Li, Haifang Abbasi, Adeel A. Lone, Kashif J. Zhao, Zirun Zaib, Mahnoor Chen, Anne Duong, Tim Q. |
author_sort | Hussain, Lal |
collection | PubMed |
description | BACKGROUND: The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. PURPOSE: The study aimed at developing an AI imaging analysis tool to classify COVID-19 lung infection based on portable CXRs. MATERIALS AND METHODS: Public datasets of COVID-19 (N = 130), bacterial pneumonia (N = 145), non-COVID-19 viral pneumonia (N = 145), and normal (N = 138) CXRs were analyzed. Texture and morphological features were extracted. Five supervised machine-learning AI algorithms were used to classify COVID-19 from other conditions. Two-class and multi-class classification were performed. Statistical analysis was done using unpaired two-tailed t tests with unequal variance between groups. Performance of classification models used the receiver-operating characteristic (ROC) curve analysis. RESULTS: For the two-class classification, the accuracy, sensitivity and specificity were, respectively, 100%, 100%, and 100% for COVID-19 vs normal; 96.34%, 95.35% and 97.44% for COVID-19 vs bacterial pneumonia; and 97.56%, 97.44% and 97.67% for COVID-19 vs non-COVID-19 viral pneumonia. For the multi-class classification, the combined accuracy and AUC were 79.52% and 0.87, respectively. CONCLUSION: AI classification of texture and morphological features of portable CXRs accurately distinguishes COVID-19 lung infection in patients in multi-class datasets. Deep-learning methods have the potential to improve diagnostic efficiency and accuracy for portable CXRs. |
format | Online Article Text |
id | pubmed-7686836 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-76868362020-11-25 Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection Hussain, Lal Nguyen, Tony Li, Haifang Abbasi, Adeel A. Lone, Kashif J. Zhao, Zirun Zaib, Mahnoor Chen, Anne Duong, Tim Q. Biomed Eng Online Research BACKGROUND: The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. PURPOSE: The study aimed at developing an AI imaging analysis tool to classify COVID-19 lung infection based on portable CXRs. MATERIALS AND METHODS: Public datasets of COVID-19 (N = 130), bacterial pneumonia (N = 145), non-COVID-19 viral pneumonia (N = 145), and normal (N = 138) CXRs were analyzed. Texture and morphological features were extracted. Five supervised machine-learning AI algorithms were used to classify COVID-19 from other conditions. Two-class and multi-class classification were performed. Statistical analysis was done using unpaired two-tailed t tests with unequal variance between groups. Performance of classification models used the receiver-operating characteristic (ROC) curve analysis. RESULTS: For the two-class classification, the accuracy, sensitivity and specificity were, respectively, 100%, 100%, and 100% for COVID-19 vs normal; 96.34%, 95.35% and 97.44% for COVID-19 vs bacterial pneumonia; and 97.56%, 97.44% and 97.67% for COVID-19 vs non-COVID-19 viral pneumonia. For the multi-class classification, the combined accuracy and AUC were 79.52% and 0.87, respectively. CONCLUSION: AI classification of texture and morphological features of portable CXRs accurately distinguishes COVID-19 lung infection in patients in multi-class datasets. Deep-learning methods have the potential to improve diagnostic efficiency and accuracy for portable CXRs. BioMed Central 2020-11-25 /pmc/articles/PMC7686836/ /pubmed/33239006 http://dx.doi.org/10.1186/s12938-020-00831-x Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Hussain, Lal Nguyen, Tony Li, Haifang Abbasi, Adeel A. Lone, Kashif J. Zhao, Zirun Zaib, Mahnoor Chen, Anne Duong, Tim Q. Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection |
title | Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection |
title_full | Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection |
title_fullStr | Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection |
title_full_unstemmed | Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection |
title_short | Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection |
title_sort | machine-learning classification of texture features of portable chest x-ray accurately classifies covid-19 lung infection |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7686836/ https://www.ncbi.nlm.nih.gov/pubmed/33239006 http://dx.doi.org/10.1186/s12938-020-00831-x |
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