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Article

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

1
School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK
2
Department of Computer Science, Durham University, Durham DH1 3LE, UK
3
Faculty of Science, Northern Border University, Arar 91431, Saudi Arabia
*
Author to whom correspondence should be addressed.
Academic Editor: Paweł Pławiak
Sensors 2021, 21(17), 5702; https://doi.org/10.3390/s21175702
Received: 28 July 2021 / Revised: 17 August 2021 / Accepted: 20 August 2021 / Published: 24 August 2021
(This article belongs to the Section Sensing and Imaging)
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. View Full-Text
Keywords: COVID-19; chest X-ray; deep learning; CNN; image classification COVID-19; chest X-ray; deep learning; CNN; image classification
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MDPI and ACS Style

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

AMA Style

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

Chicago/Turabian Style

Okolo, Gabriel Iluebe, Stamos Katsigiannis, Turke Althobaiti, and Naeem Ramzan. 2021. "On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays" Sensors 21, no. 17: 5702. https://doi.org/10.3390/s21175702

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