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X-ray and CT-scan-based automated detection and classification of covid-19 using convolutional neural networks (CNN)

Covid-19 (Coronavirus Disease-2019) is the most recent coronavirus-related disease that has been announced as a pandemic by the World Health Organization (WHO). Furthermore, it has brought the whole planet to a halt as a result of the worldwide introduction of lockdown and killed millions of people....

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Autores principales: Thakur, Samritika, Kumar, Aman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8241644/
https://www.ncbi.nlm.nih.gov/pubmed/34226832
http://dx.doi.org/10.1016/j.bspc.2021.102920
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author Thakur, Samritika
Kumar, Aman
author_facet Thakur, Samritika
Kumar, Aman
author_sort Thakur, Samritika
collection PubMed
description Covid-19 (Coronavirus Disease-2019) is the most recent coronavirus-related disease that has been announced as a pandemic by the World Health Organization (WHO). Furthermore, it has brought the whole planet to a halt as a result of the worldwide introduction of lockdown and killed millions of people. While this virus has a low fatality rate, the problem is that it is highly infectious, and as a result, it has infected a large number of people, putting a strain on the healthcare system, hence, Covid-19 identification in patients has become critical. The goal of this research is to use X-rays images and computed tomography (CT) images to introduce a deep learning strategy based on the Convolutional Neural Network (CNN) to automatically detect and identify the Covid-19 disease. We have implemented two different classifications using CNN, i.e., binary and multiclass classification. A total of 3,877 images dataset of CT and X-ray images has been utilised to train the model in binary classification, out of which the 1,917 images are of Covid-19 infected individuals . An overall accuracy of 99.64%, recall (or sensitivity) of 99.58%, the precision of 99.56%, F1-score of 99.59%, and ROC of 100% has been observed for the binary classification. For multiple classifications, the model has been trained using a total of 6,077 images, out of which 1,917 images are of Covid-19 infected people, 1,960 images are of normal healthy people, and 2,200 images are of pneumonia infected people. An accuracy of 98.28%, recall (or sensitivity) of 98.25%, the precision of 98.22%, F1-score of 98.23%, and ROC of 99.87% has been achieved for the multiclass classification using the proposed method. On the currently available dataset, the our proposed model produced the desired results, and it can assist healthcare workers in quickly detecting Covid-19 positive patients.
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spelling pubmed-82416442021-07-01 X-ray and CT-scan-based automated detection and classification of covid-19 using convolutional neural networks (CNN) Thakur, Samritika Kumar, Aman Biomed Signal Process Control Article Covid-19 (Coronavirus Disease-2019) is the most recent coronavirus-related disease that has been announced as a pandemic by the World Health Organization (WHO). Furthermore, it has brought the whole planet to a halt as a result of the worldwide introduction of lockdown and killed millions of people. While this virus has a low fatality rate, the problem is that it is highly infectious, and as a result, it has infected a large number of people, putting a strain on the healthcare system, hence, Covid-19 identification in patients has become critical. The goal of this research is to use X-rays images and computed tomography (CT) images to introduce a deep learning strategy based on the Convolutional Neural Network (CNN) to automatically detect and identify the Covid-19 disease. We have implemented two different classifications using CNN, i.e., binary and multiclass classification. A total of 3,877 images dataset of CT and X-ray images has been utilised to train the model in binary classification, out of which the 1,917 images are of Covid-19 infected individuals . An overall accuracy of 99.64%, recall (or sensitivity) of 99.58%, the precision of 99.56%, F1-score of 99.59%, and ROC of 100% has been observed for the binary classification. For multiple classifications, the model has been trained using a total of 6,077 images, out of which 1,917 images are of Covid-19 infected people, 1,960 images are of normal healthy people, and 2,200 images are of pneumonia infected people. An accuracy of 98.28%, recall (or sensitivity) of 98.25%, the precision of 98.22%, F1-score of 98.23%, and ROC of 99.87% has been achieved for the multiclass classification using the proposed method. On the currently available dataset, the our proposed model produced the desired results, and it can assist healthcare workers in quickly detecting Covid-19 positive patients. Elsevier Ltd. 2021-08 2021-06-30 /pmc/articles/PMC8241644/ /pubmed/34226832 http://dx.doi.org/10.1016/j.bspc.2021.102920 Text en © 2021 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Thakur, Samritika
Kumar, Aman
X-ray and CT-scan-based automated detection and classification of covid-19 using convolutional neural networks (CNN)
title X-ray and CT-scan-based automated detection and classification of covid-19 using convolutional neural networks (CNN)
title_full X-ray and CT-scan-based automated detection and classification of covid-19 using convolutional neural networks (CNN)
title_fullStr X-ray and CT-scan-based automated detection and classification of covid-19 using convolutional neural networks (CNN)
title_full_unstemmed X-ray and CT-scan-based automated detection and classification of covid-19 using convolutional neural networks (CNN)
title_short X-ray and CT-scan-based automated detection and classification of covid-19 using convolutional neural networks (CNN)
title_sort x-ray and ct-scan-based automated detection and classification of covid-19 using convolutional neural networks (cnn)
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8241644/
https://www.ncbi.nlm.nih.gov/pubmed/34226832
http://dx.doi.org/10.1016/j.bspc.2021.102920
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