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Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning
PURPOSE: Coronavirus 2019 (COVID-19) had major social, medical, and economic impacts globally. The study aims to develop a deep-learning model that can predict the severity of COVID-19 in patients based on CT images of their lungs. METHODS: COVID-19 causes lung infections, and qRT-PCR is an essentia...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10010231/ https://www.ncbi.nlm.nih.gov/pubmed/37077696 http://dx.doi.org/10.1007/s40846-023-00783-2 |
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author | Alaiad, Ahmad Imwafak Mugdadi, Esraa Ahmad Hmeidi, Ismail Ibrahim Obeidat, Naser Abualigah, Laith |
author_facet | Alaiad, Ahmad Imwafak Mugdadi, Esraa Ahmad Hmeidi, Ismail Ibrahim Obeidat, Naser Abualigah, Laith |
author_sort | Alaiad, Ahmad Imwafak |
collection | PubMed |
description | PURPOSE: Coronavirus 2019 (COVID-19) had major social, medical, and economic impacts globally. The study aims to develop a deep-learning model that can predict the severity of COVID-19 in patients based on CT images of their lungs. METHODS: COVID-19 causes lung infections, and qRT-PCR is an essential tool used to detect virus infection. However, qRT-PCR is inadequate for detecting the severity of the disease and the extent to which it affects the lung. In this paper, we aim to determine the severity level of COVID-19 by studying lung CT scans of people diagnosed with the virus. RESULTS: We used images from King Abdullah University Hospital in Jordan; we collected our dataset from 875 cases with 2205 CT images. A radiologist classified the images into four levels of severity: normal, mild, moderate, and severe. We used various deep-learning algorithms to predict the severity of lung diseases. The results show that the best deep-learning algorithm used is Resnet101, with an accuracy score of 99.5% and a data loss rate of 0.03%. CONCLUSION: The proposed model assisted in diagnosing and treating COVID-19 patients and helped improve patient outcomes. |
format | Online Article Text |
id | pubmed-10010231 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-100102312023-03-14 Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning Alaiad, Ahmad Imwafak Mugdadi, Esraa Ahmad Hmeidi, Ismail Ibrahim Obeidat, Naser Abualigah, Laith J Med Biol Eng Original Article PURPOSE: Coronavirus 2019 (COVID-19) had major social, medical, and economic impacts globally. The study aims to develop a deep-learning model that can predict the severity of COVID-19 in patients based on CT images of their lungs. METHODS: COVID-19 causes lung infections, and qRT-PCR is an essential tool used to detect virus infection. However, qRT-PCR is inadequate for detecting the severity of the disease and the extent to which it affects the lung. In this paper, we aim to determine the severity level of COVID-19 by studying lung CT scans of people diagnosed with the virus. RESULTS: We used images from King Abdullah University Hospital in Jordan; we collected our dataset from 875 cases with 2205 CT images. A radiologist classified the images into four levels of severity: normal, mild, moderate, and severe. We used various deep-learning algorithms to predict the severity of lung diseases. The results show that the best deep-learning algorithm used is Resnet101, with an accuracy score of 99.5% and a data loss rate of 0.03%. CONCLUSION: The proposed model assisted in diagnosing and treating COVID-19 patients and helped improve patient outcomes. Springer Berlin Heidelberg 2023-03-13 2023 /pmc/articles/PMC10010231/ /pubmed/37077696 http://dx.doi.org/10.1007/s40846-023-00783-2 Text en © Taiwanese Society of Biomedical Engineering 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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 | Original Article Alaiad, Ahmad Imwafak Mugdadi, Esraa Ahmad Hmeidi, Ismail Ibrahim Obeidat, Naser Abualigah, Laith Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning |
title | Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning |
title_full | Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning |
title_fullStr | Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning |
title_full_unstemmed | Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning |
title_short | Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning |
title_sort | predicting the severity of covid-19 from lung ct images using novel deep learning |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10010231/ https://www.ncbi.nlm.nih.gov/pubmed/37077696 http://dx.doi.org/10.1007/s40846-023-00783-2 |
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