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

Article Subjects > Biomedicine Europe University of Atlantic > Research > Articles and books
Ibero-american International University > Research > Scientific Production Abierto Inglés 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. metadata Kaur, Prabhjot and Harnal, Shilpi and Tiwari, Rajeev and Alharithi, Fahd S. and Almulihi, Ahmed H. and Delgado Noya, Irene and Goyal, Nitin mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, irene.delgado@uneatlantico.es, UNSPECIFIED (2021) A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images. International Journal of Environmental Research and Public Health, 18 (22). p. 12191. ISSN 1660-4601

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Official URL: http://doi.org/10.3390/ijerph182212191

Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	convolutional neural network; COVID-19; disease detection; InceptionV4; SVM; chest XR images
Subjects:	Subjects > Biomedicine
Divisions:	Europe University of Atlantic > Research > Articles and books Ibero-american International University > Research > Scientific Production
Date Deposited:	23 Mar 2022 19:44
Last Modified:	20 Mar 2025 20:06
URI:	https://repositorio.uneatlantico.es/id/eprint/530

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

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