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On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays

Okolo, Gabriel Iluebe; Katsigiannis, Stamos; Althobaiti, Turke; Ramzan, Naeem

On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays Thumbnail


Authors

Gabriel Iluebe Okolo

Turke Althobaiti

Naeem Ramzan



Abstract

The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated the imperative need for quick, inexpensive, accessible and reliable diagnostic methods that would allow the detection of infected individuals with minimal resources. Radiography, and more specifically, chest radiography, is a relatively inexpensive medical imaging modality that can potentially offer a solution for the diagnosis of COVID-19 cases. In this work, we examined eleven deep convolutional neural network architectures for the task of classifying chest X-ray images as belonging to healthy individuals, individuals with COVID-19 or individuals with viral pneumonia. All the examined networks are established architectures that have been proven to be efficient in image classification tasks, and we evaluated three different adjustments to modify the architectures for the task at hand by expanding them with additional layers. The proposed approaches were evaluated for all the examined architectures on a dataset with real chest X-ray images, reaching the highest classification accuracy of 98.04% and the highest F1-score of 98.22% for the best-performing setting.

Citation

Okolo, G. I., Katsigiannis, S., Althobaiti, T., & Ramzan, N. (2021). On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays. Sensors, 21(17), Article 5702. https://doi.org/10.3390/s21175702

Journal Article Type Article
Acceptance Date Aug 20, 2021
Online Publication Date Aug 24, 2021
Publication Date Sep 1, 2021
Deposit Date Sep 6, 2021
Publicly Available Date Sep 6, 2021
Journal Sensors
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 21
Issue 17
Article Number 5702
DOI https://doi.org/10.3390/s21175702

Files

Accepted Journal Article (4.4 Mb)
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.







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