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Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images
The number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on peo...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858730/ https://www.ncbi.nlm.nih.gov/pubmed/36674023 http://dx.doi.org/10.3390/ijerph20021268 |
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author | Hayat, Ahatsham Baglat, Preety Mendonça, Fábio Mostafa, Sheikh Shanawaz Morgado-Dias, Fernando |
author_facet | Hayat, Ahatsham Baglat, Preety Mendonça, Fábio Mostafa, Sheikh Shanawaz Morgado-Dias, Fernando |
author_sort | Hayat, Ahatsham |
collection | PubMed |
description | The number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on people’s health and the economy worldwide. For COVID-19 detection, reverse transcription-polymerase chain reaction testing is the benchmark. However, this test takes a long time and necessitates a lot of laboratory resources. A new trend is emerging to address these limitations regarding the use of machine learning and deep learning techniques for automatic analysis, as these can attain high diagnosis results, especially by using medical imaging techniques. However, a key question arises whether a chest computed tomography scan or chest X-ray can be used for COVID-19 detection. A total of 17,599 images were examined in this work to develop the models used to classify the occurrence of COVID-19 infection, while four different classifiers were studied. These are the convolutional neural network (proposed architecture (named, SCovNet) and Resnet18), support vector machine, and logistic regression. Out of all four models, the proposed SCoVNet architecture reached the best performance with an accuracy of almost 99% and 98% on chest computed tomography scan images and chest X-ray images, respectively. |
format | Online Article Text |
id | pubmed-9858730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98587302023-01-21 Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images Hayat, Ahatsham Baglat, Preety Mendonça, Fábio Mostafa, Sheikh Shanawaz Morgado-Dias, Fernando Int J Environ Res Public Health Article The number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on people’s health and the economy worldwide. For COVID-19 detection, reverse transcription-polymerase chain reaction testing is the benchmark. However, this test takes a long time and necessitates a lot of laboratory resources. A new trend is emerging to address these limitations regarding the use of machine learning and deep learning techniques for automatic analysis, as these can attain high diagnosis results, especially by using medical imaging techniques. However, a key question arises whether a chest computed tomography scan or chest X-ray can be used for COVID-19 detection. A total of 17,599 images were examined in this work to develop the models used to classify the occurrence of COVID-19 infection, while four different classifiers were studied. These are the convolutional neural network (proposed architecture (named, SCovNet) and Resnet18), support vector machine, and logistic regression. Out of all four models, the proposed SCoVNet architecture reached the best performance with an accuracy of almost 99% and 98% on chest computed tomography scan images and chest X-ray images, respectively. MDPI 2023-01-10 /pmc/articles/PMC9858730/ /pubmed/36674023 http://dx.doi.org/10.3390/ijerph20021268 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hayat, Ahatsham Baglat, Preety Mendonça, Fábio Mostafa, Sheikh Shanawaz Morgado-Dias, Fernando Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images |
title | Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images |
title_full | Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images |
title_fullStr | Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images |
title_full_unstemmed | Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images |
title_short | Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images |
title_sort | novel comparative study for the detection of covid-19 using ct scan and chest x-ray images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858730/ https://www.ncbi.nlm.nih.gov/pubmed/36674023 http://dx.doi.org/10.3390/ijerph20021268 |
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