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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8618754 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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