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Firecovnet: a novel, lightweight, and fast deep learning-based network for detecting COVID-19 patients using chest x-rays

At the end of 2019, a new virus (SARS-CoV-2) called COVID-19 was reported in Wuhan, China, and spread rapidly worldwide. After two years later, several variants of this virus were created, infecting 608 million people and causing 6.51 million deaths. Due to the insufficient sensitivity of RT-PCR tes...

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Main Authors: Leila Hassanlou, Saeed Meshgini, Reza Afrouzian, Ali Farzamnia, Ervin Gubin Moung
Format: Article
Language:English
Published: Multidisciplinary Digital Publishing Institute (MDPI) 2022
Subjects:
RA1-1270 Public aspects of medicine
RC705-779 Diseases of the respiratory system
Online Access:https://eprints.ums.edu.my/id/eprint/42306/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/42306/
https://doi.org/10.3390/electronics11193068
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spelling my.ums.eprints.423062024-12-16T04:03:11Z https://eprints.ums.edu.my/id/eprint/42306/ Firecovnet: a novel, lightweight, and fast deep learning-based network for detecting COVID-19 patients using chest x-rays Leila Hassanlou Saeed Meshgini Reza Afrouzian Ali Farzamnia Ervin Gubin Moung RA1-1270 Public aspects of medicine RC705-779 Diseases of the respiratory system At the end of 2019, a new virus (SARS-CoV-2) called COVID-19 was reported in Wuhan, China, and spread rapidly worldwide. After two years later, several variants of this virus were created, infecting 608 million people and causing 6.51 million deaths. Due to the insufficient sensitivity of RT-PCR test kits, one of the main tools for detecting the virus, chest X-ray images are a popular tool for diagnosing the virus in patients with respiratory symptoms. Models based on deep learning are showing promising results in combating this pandemic. A novel convolutional neural network, FirecovNet, is suggested in this study that detects COVID-19 infection automatically based on raw chest X-ray images. With an architecture inspired by the integration of DarkNet and SqueezeNet networks, the proposed model has fewer parameters than state-of-the-art models and is trained using COVID-19, bacterial pneumonia, normal, lung opacity, and viral pneumonia images, which were collected from two public datasets and also are symmetric in the distribution in class. FirecovNet performance has been verified using the stratified 5-fold cross-validation method. A total of five classification tasks are performed, including four 4-class classifications, and one 5-class classification, and the accuracy of all tasks was at least 95.9%. For all classification tasks, the proposed network has demonstrated promising results in precision, sensitivity, and F1-score. Moreover, a comparison was made between the proposed network and eight deep transfer learning networks and in terms of accuracy, precision, sensitivity, F1-score, speed, and size of the saved model; FirecovNet was very promising. Therefore, FirecovNet can be useful as a tool for more accurate diagnosis of the COVID-19 virus, along with diagnostic tests, in situations where the number of specialist radiologists may be limited. Multidisciplinary Digital Publishing Institute (MDPI) 2022 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/42306/2/FULL%20TEXT.pdf Leila Hassanlou and Saeed Meshgini and Reza Afrouzian and Ali Farzamnia and Ervin Gubin Moung (2022) Firecovnet: a novel, lightweight, and fast deep learning-based network for detecting COVID-19 patients using chest x-rays. Electronics, 11. pp. 1-20. https://doi.org/10.3390/electronics11193068
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
topic RA1-1270 Public aspects of medicine
RC705-779 Diseases of the respiratory system
spellingShingle RA1-1270 Public aspects of medicine
RC705-779 Diseases of the respiratory system
Leila Hassanlou
Saeed Meshgini
Reza Afrouzian
Ali Farzamnia
Ervin Gubin Moung
Firecovnet: a novel, lightweight, and fast deep learning-based network for detecting COVID-19 patients using chest x-rays
description At the end of 2019, a new virus (SARS-CoV-2) called COVID-19 was reported in Wuhan, China, and spread rapidly worldwide. After two years later, several variants of this virus were created, infecting 608 million people and causing 6.51 million deaths. Due to the insufficient sensitivity of RT-PCR test kits, one of the main tools for detecting the virus, chest X-ray images are a popular tool for diagnosing the virus in patients with respiratory symptoms. Models based on deep learning are showing promising results in combating this pandemic. A novel convolutional neural network, FirecovNet, is suggested in this study that detects COVID-19 infection automatically based on raw chest X-ray images. With an architecture inspired by the integration of DarkNet and SqueezeNet networks, the proposed model has fewer parameters than state-of-the-art models and is trained using COVID-19, bacterial pneumonia, normal, lung opacity, and viral pneumonia images, which were collected from two public datasets and also are symmetric in the distribution in class. FirecovNet performance has been verified using the stratified 5-fold cross-validation method. A total of five classification tasks are performed, including four 4-class classifications, and one 5-class classification, and the accuracy of all tasks was at least 95.9%. For all classification tasks, the proposed network has demonstrated promising results in precision, sensitivity, and F1-score. Moreover, a comparison was made between the proposed network and eight deep transfer learning networks and in terms of accuracy, precision, sensitivity, F1-score, speed, and size of the saved model; FirecovNet was very promising. Therefore, FirecovNet can be useful as a tool for more accurate diagnosis of the COVID-19 virus, along with diagnostic tests, in situations where the number of specialist radiologists may be limited.
format Article
author Leila Hassanlou
Saeed Meshgini
Reza Afrouzian
Ali Farzamnia
Ervin Gubin Moung
author_facet Leila Hassanlou
Saeed Meshgini
Reza Afrouzian
Ali Farzamnia
Ervin Gubin Moung
author_sort Leila Hassanlou
title Firecovnet: a novel, lightweight, and fast deep learning-based network for detecting COVID-19 patients using chest x-rays
title_short Firecovnet: a novel, lightweight, and fast deep learning-based network for detecting COVID-19 patients using chest x-rays
title_full Firecovnet: a novel, lightweight, and fast deep learning-based network for detecting COVID-19 patients using chest x-rays
title_fullStr Firecovnet: a novel, lightweight, and fast deep learning-based network for detecting COVID-19 patients using chest x-rays
title_full_unstemmed Firecovnet: a novel, lightweight, and fast deep learning-based network for detecting COVID-19 patients using chest x-rays
title_sort firecovnet: a novel, lightweight, and fast deep learning-based network for detecting covid-19 patients using chest x-rays
publisher Multidisciplinary Digital Publishing Institute (MDPI)
publishDate 2022
url https://eprints.ums.edu.my/id/eprint/42306/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/42306/
https://doi.org/10.3390/electronics11193068
_version_ 1818835203879075840
score 13.223943

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