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:	Prabhjot Kaur, Shilpi Harnal, Rajeev Tiwari, Fahd S. Alharithi, Ahmed H. Almulihi, Irene Delgado Noya, Nitin Goyal
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	convolutional neural network COVID-19 disease detection InceptionV4 SVM chest XR images Medicine R
Acceso en línea:	https://doaj.org/article/5712c5a16ea743869ed127dfd389c15e
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id	oai:doaj.org-article:5712c5a16ea743869ed127dfd389c15e
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spelling	oai:doaj.org-article:5712c5a16ea743869ed127dfd389c15e2021-11-25T17:51:58ZA Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images10.3390/ijerph1822121911660-46011661-7827https://doaj.org/article/5712c5a16ea743869ed127dfd389c15e2021-11-01T00:00:00Zhttps://www.mdpi.com/1660-4601/18/22/12191https://doaj.org/toc/1661-7827https://doaj.org/toc/1660-4601COVID-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.Prabhjot KaurShilpi HarnalRajeev TiwariFahd S. AlharithiAhmed H. AlmulihiIrene Delgado NoyaNitin GoyalMDPI AGarticleconvolutional neural networkCOVID-19disease detectionInceptionV4SVMchest XR imagesMedicineRENInternational Journal of Environmental Research and Public Health, Vol 18, Iss 12191, p 12191 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	convolutional neural network COVID-19 disease detection InceptionV4 SVM chest XR images Medicine R
spellingShingle	convolutional neural network COVID-19 disease detection InceptionV4 SVM chest XR images Medicine R Prabhjot Kaur Shilpi Harnal Rajeev Tiwari Fahd S. Alharithi Ahmed H. Almulihi Irene Delgado Noya Nitin Goyal A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images
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.
format	article
author	Prabhjot Kaur Shilpi Harnal Rajeev Tiwari Fahd S. Alharithi Ahmed H. Almulihi Irene Delgado Noya Nitin Goyal
author_facet	Prabhjot Kaur Shilpi Harnal Rajeev Tiwari Fahd S. Alharithi Ahmed H. Almulihi Irene Delgado Noya Nitin Goyal
author_sort	Prabhjot Kaur
title	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_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_sort	hybrid convolutional neural network model for diagnosis of covid-19 using chest x-ray images
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5712c5a16ea743869ed127dfd389c15e
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A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images

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