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A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images

COVID-19 declared as a pandemic that has a faster rate of infection and has impacted the lives and the country’s economy due to forced lockdowns. Its detection using RT-PCR is required long time and due to which its infection has grown exponentially. This creates havoc for the shortage of testing ki...

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Autores principales: Kaur, Prabhjot, Harnal, Shilpi, Tiwari, Rajeev, Alharithi, Fahd S., Almulihi, Ahmed H., Noya, Irene Delgado, Goyal, Nitin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8618754/
https://www.ncbi.nlm.nih.gov/pubmed/34831960
http://dx.doi.org/10.3390/ijerph182212191
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author Kaur, Prabhjot
Harnal, Shilpi
Tiwari, Rajeev
Alharithi, Fahd S.
Almulihi, Ahmed H.
Noya, Irene Delgado
Goyal, Nitin
author_facet Kaur, Prabhjot
Harnal, Shilpi
Tiwari, Rajeev
Alharithi, Fahd S.
Almulihi, Ahmed H.
Noya, Irene Delgado
Goyal, Nitin
author_sort Kaur, Prabhjot
collection PubMed
description COVID-19 declared as a pandemic that has a faster rate of infection and has impacted the lives and the country’s economy due to forced lockdowns. Its detection using RT-PCR is required long time and due to which its infection has grown exponentially. This creates havoc for the shortage of testing kits in many countries. This work has proposed a new image processing-based technique for the health care systems named “C19D-Net”, to detect “COVID-19” infection from “Chest X-Ray” (XR) images, which can help radiologists to improve their accuracy of detection COVID-19. The proposed system extracts deep learning (DL) features by applying the InceptionV4 architecture and Multiclass SVM classifier to classify and detect COVID-19 infection into four different classes. The dataset of 1900 Chest XR images has been collected from two publicly accessible databases. Images are pre-processed with proper scaling and regular feeding to the proposed model for accuracy attainments. Extensive tests are conducted with the proposed model (“C19D-Net”) and it has succeeded to achieve the highest COVID-19 detection accuracy as 96.24% for 4-classes, 95.51% for three-classes, and 98.1% for two-classes. The proposed method has outperformed well in expressions of “precision”, “accuracy”, “F1-score” and “recall” in comparison with most of the recent previously published methods. As a result, for the present situation of COVID-19, the proposed “C19D-Net” can be employed in places where test kits are in short supply, to help the radiologists to improve their accuracy of detection of COVID-19 patients through XR-Images.
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spelling pubmed-86187542021-11-27 A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images Kaur, Prabhjot Harnal, Shilpi Tiwari, Rajeev Alharithi, Fahd S. Almulihi, Ahmed H. Noya, Irene Delgado Goyal, Nitin Int J Environ Res Public Health Article COVID-19 declared as a pandemic that has a faster rate of infection and has impacted the lives and the country’s economy due to forced lockdowns. Its detection using RT-PCR is required long time and due to which its infection has grown exponentially. This creates havoc for the shortage of testing kits in many countries. This work has proposed a new image processing-based technique for the health care systems named “C19D-Net”, to detect “COVID-19” infection from “Chest X-Ray” (XR) images, which can help radiologists to improve their accuracy of detection COVID-19. The proposed system extracts deep learning (DL) features by applying the InceptionV4 architecture and Multiclass SVM classifier to classify and detect COVID-19 infection into four different classes. The dataset of 1900 Chest XR images has been collected from two publicly accessible databases. Images are pre-processed with proper scaling and regular feeding to the proposed model for accuracy attainments. Extensive tests are conducted with the proposed model (“C19D-Net”) and it has succeeded to achieve the highest COVID-19 detection accuracy as 96.24% for 4-classes, 95.51% for three-classes, and 98.1% for two-classes. The proposed method has outperformed well in expressions of “precision”, “accuracy”, “F1-score” and “recall” in comparison with most of the recent previously published methods. As a result, for the present situation of COVID-19, the proposed “C19D-Net” can be employed in places where test kits are in short supply, to help the radiologists to improve their accuracy of detection of COVID-19 patients through XR-Images. MDPI 2021-11-20 /pmc/articles/PMC8618754/ /pubmed/34831960 http://dx.doi.org/10.3390/ijerph182212191 Text en © 2021 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
Kaur, Prabhjot
Harnal, Shilpi
Tiwari, Rajeev
Alharithi, Fahd S.
Almulihi, Ahmed H.
Noya, Irene Delgado
Goyal, Nitin
A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images
title A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images
title_full A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images
title_fullStr A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images
title_full_unstemmed A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images
title_short A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images
title_sort hybrid convolutional neural network model for diagnosis of covid-19 using chest x-ray images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8618754/
https://www.ncbi.nlm.nih.gov/pubmed/34831960
http://dx.doi.org/10.3390/ijerph182212191
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