Okolo, Gabriel Iluebe and Katsigiannis, Stamos and Althobaiti, Turke and Ramzan, Naeem (2021) 'On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays.', Sensors., 21 (17). p. 5702.
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.
|Full text:||(AM) Accepted Manuscript|
Available under License - Creative Commons Attribution 4.0.
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|Publisher Web site:||https://doi.org/10.3390/s21175702|
|Publisher 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.|
|Date accepted:||20 August 2021|
|Date deposited:||06 September 2021|
|Date of first online publication:||24 August 2021|
|Date first made open access:||06 September 2021|
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