COVIDetection-Net : A tailored COVID-19 detection from chest radiography images using deep learning
© 2021 Elsevier GmbH. All rights reserved..
In this study, a medical system based on Deep Learning (DL) which we called "COVIDetection-Net" is proposed for automatic detection of new corona virus disease 2019 (COVID-19) infection from chest radiography images (CRIs). The proposed system is based on ShuffleNet and SqueezeNet architecture to extract deep learned features and Multiclass Support Vector Machines (MSVM) for detection and classification. Our dataset contains 1200 CRIs that collected from two different publicly available databases. Extensive experiments were carried out using the proposed model. The highest detection accuracy of 100 % for COVID/NonCOVID, 99.72 % for COVID/Normal/pneumonia and 94.44 % for COVID/Normal/Bacterial pneumonia/Viral pneumonia have been obtained. The proposed system superior all published methods in recall, specificity, precision, F1-Score and accuracy. Confusion Matrix (CM) and Receiver Operation Characteristics (ROC) analysis are also used to depict the performance of the proposed model. Hence the proposed COVIDetection-Net can serve as an efficient system in the current state of COVID-19 pandemic and can be used in everywhere that are facing shortage of test kits.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:231 |
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| Enthalten in: |
Optik - 231(2021) vom: 08. Apr., Seite 166405 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Elkorany, Ahmed S [VerfasserIn] |
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| Links: |
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| Themen: |
Convolutional neural network |
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| Anmerkungen: |
Date Revised 03.03.2021 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.ijleo.2021.166405 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a © 2021 Elsevier GmbH. All rights reserved. | ||
| 520 | |a In this study, a medical system based on Deep Learning (DL) which we called "COVIDetection-Net" is proposed for automatic detection of new corona virus disease 2019 (COVID-19) infection from chest radiography images (CRIs). The proposed system is based on ShuffleNet and SqueezeNet architecture to extract deep learned features and Multiclass Support Vector Machines (MSVM) for detection and classification. Our dataset contains 1200 CRIs that collected from two different publicly available databases. Extensive experiments were carried out using the proposed model. The highest detection accuracy of 100 % for COVID/NonCOVID, 99.72 % for COVID/Normal/pneumonia and 94.44 % for COVID/Normal/Bacterial pneumonia/Viral pneumonia have been obtained. The proposed system superior all published methods in recall, specificity, precision, F1-Score and accuracy. Confusion Matrix (CM) and Receiver Operation Characteristics (ROC) analysis are also used to depict the performance of the proposed model. Hence the proposed COVIDetection-Net can serve as an efficient system in the current state of COVID-19 pandemic and can be used in everywhere that are facing shortage of test kits | ||
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