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Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images

The novel coronavirus (COVID-19) could be described as the greatest human challenge of the 21st century. The development and transmission of the disease have increased mortality in all countries. Therefore, a rapid diagnosis of COVID-19 is necessary to treat and control the disease. In this paper, a...

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Main Authors: Sobhan Sheykhivand, Zohreh Mousavi, Sina Mojtahedi, Tohid Yousefi Rezaii, Ali Farzamnia, Saeed Meshgini, Ismail Saad
Format: Article
Language:English
English
Published: Elsevier BV 2021
Subjects:
R Medicine (General)
Online Access:https://eprints.ums.edu.my/id/eprint/26764/1/Developing%20an%20efficient%20deep%20neural%20network%20for%20automatic%20detection%20of%20COVID-19%20using%20chest%20X-ray%20images.pdf
https://eprints.ums.edu.my/id/eprint/26764/2/Developing%20an%20efficient%20deep%20neural%20network%20for%20automatic%20detection%20of%20COVID-19%20using%20chest%20X-ray%20images.pdf
https://eprints.ums.edu.my/id/eprint/26764/
https://doi.org/10.1016/j.aej.2021.01.011
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spelling my.ums.eprints.267642021-04-16T06:18:47Z https://eprints.ums.edu.my/id/eprint/26764/ Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images Sobhan Sheykhivand Zohreh Mousavi Sina Mojtahedi Tohid Yousefi Rezaii Ali Farzamnia Saeed Meshgini Ismail Saad R Medicine (General) The novel coronavirus (COVID-19) could be described as the greatest human challenge of the 21st century. The development and transmission of the disease have increased mortality in all countries. Therefore, a rapid diagnosis of COVID-19 is necessary to treat and control the disease. In this paper, a new method for the automatic identification of pneumonia (including COVID-19) is presented using a proposed deep neural network. In the proposed method, the chest X-ray images are used to separate 2–4 classes in 7 different and functional scenarios according to healthy, viral, bacterial, and COVID-19 classes. In the proposed architecture, Generative Adversarial Networks (GANs) are used together with a fusion of the deep transfer learning and LSTM networks, without involving feature extraction/selection for classification of pneumonia. We have achieved more than 90% accuracy for all scenarios except one and also achieved 99% accuracy for separating COVID 19 from healthy group. We also compared our deep proposed network with other deep transfer learning networks (including Inception-ResNet V2, Inception V4, VGG16 and MobileNet) that have been recently widely used in pneumonia detection studies. The results based on the proposed network were very promising in terms of accuracy, precision, sensitivity, and specificity compared to the other deep transfer learning approaches. Depending on the high performance of the proposed method, it can be used during the treatment of patients. Elsevier BV 2021-01-21 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/26764/1/Developing%20an%20efficient%20deep%20neural%20network%20for%20automatic%20detection%20of%20COVID-19%20using%20chest%20X-ray%20images.pdf text en https://eprints.ums.edu.my/id/eprint/26764/2/Developing%20an%20efficient%20deep%20neural%20network%20for%20automatic%20detection%20of%20COVID-19%20using%20chest%20X-ray%20images.pdf Sobhan Sheykhivand and Zohreh Mousavi and Sina Mojtahedi and Tohid Yousefi Rezaii and Ali Farzamnia and Saeed Meshgini and Ismail Saad (2021) Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images. Alexandria Engineering Journal, 60. pp. 2885-2903. https://doi.org/10.1016/j.aej.2021.01.011
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic R Medicine (General)
spellingShingle R Medicine (General)
Sobhan Sheykhivand
Zohreh Mousavi
Sina Mojtahedi
Tohid Yousefi Rezaii
Ali Farzamnia
Saeed Meshgini
Ismail Saad
Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images
description The novel coronavirus (COVID-19) could be described as the greatest human challenge of the 21st century. The development and transmission of the disease have increased mortality in all countries. Therefore, a rapid diagnosis of COVID-19 is necessary to treat and control the disease. In this paper, a new method for the automatic identification of pneumonia (including COVID-19) is presented using a proposed deep neural network. In the proposed method, the chest X-ray images are used to separate 2–4 classes in 7 different and functional scenarios according to healthy, viral, bacterial, and COVID-19 classes. In the proposed architecture, Generative Adversarial Networks (GANs) are used together with a fusion of the deep transfer learning and LSTM networks, without involving feature extraction/selection for classification of pneumonia. We have achieved more than 90% accuracy for all scenarios except one and also achieved 99% accuracy for separating COVID 19 from healthy group. We also compared our deep proposed network with other deep transfer learning networks (including Inception-ResNet V2, Inception V4, VGG16 and MobileNet) that have been recently widely used in pneumonia detection studies. The results based on the proposed network were very promising in terms of accuracy, precision, sensitivity, and specificity compared to the other deep transfer learning approaches. Depending on the high performance of the proposed method, it can be used during the treatment of patients.
format Article
author Sobhan Sheykhivand
Zohreh Mousavi
Sina Mojtahedi
Tohid Yousefi Rezaii
Ali Farzamnia
Saeed Meshgini
Ismail Saad
author_facet Sobhan Sheykhivand
Zohreh Mousavi
Sina Mojtahedi
Tohid Yousefi Rezaii
Ali Farzamnia
Saeed Meshgini
Ismail Saad
author_sort Sobhan Sheykhivand
title Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images
title_short Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images
title_full Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images
title_fullStr Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images
title_full_unstemmed Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images
title_sort developing an efficient deep neural network for automatic detection of covid-19 using chest x-ray images
publisher Elsevier BV
publishDate 2021
url https://eprints.ums.edu.my/id/eprint/26764/1/Developing%20an%20efficient%20deep%20neural%20network%20for%20automatic%20detection%20of%20COVID-19%20using%20chest%20X-ray%20images.pdf
https://eprints.ums.edu.my/id/eprint/26764/2/Developing%20an%20efficient%20deep%20neural%20network%20for%20automatic%20detection%20of%20COVID-19%20using%20chest%20X-ray%20images.pdf
https://eprints.ums.edu.my/id/eprint/26764/
https://doi.org/10.1016/j.aej.2021.01.011
_version_ 1760230540881952768
score 13.211869

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