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Detection of COVID-19 from X-rays using hybrid deep learning models

Detection of COVID-19 from X-rays using hybrid deep learning models PurposeTo propose a model that can detect the presence of Covid-19 from chest X-rays and can be used with low hardware resource-based personal digital assistants (PDA).MethodsIn this paper, a hybrid deep learning model is proposed for the detection of coronavirus from chest X-ray images. The hybrid deep learning model is a combination of ResNet50 and MobileNet. Both ResNet50 and MobileNet are light deep neural networks (DNNs) and can be used with low hardware resource-based personal digital assistants (PDA) for quick detection of COVID-19 infection.ResultsThe performance of the proposed hybrid model is evaluated on two publicly available COVID-19 chest X-ray datasets. Both datasets include normal, pneumonia, and coronavirus-infected chest X-rays and we achieve 84.35% and 94.43% accuracy on Dataset 1 and Dataset 2 respectively.ConclusionResults show that the proposed hybrid model is better suited for COVID-19 detection. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Biomedical Engineering Springer Journals

Detection of COVID-19 from X-rays using hybrid deep learning models

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Publisher
Springer Journals
Copyright
Copyright © Sociedade Brasileira de Engenharia Biomedica 2021
ISSN
2446-4732
eISSN
2446-4740
DOI
10.1007/s42600-021-00181-0
Publisher site
See Article on Publisher Site

Abstract

PurposeTo propose a model that can detect the presence of Covid-19 from chest X-rays and can be used with low hardware resource-based personal digital assistants (PDA).MethodsIn this paper, a hybrid deep learning model is proposed for the detection of coronavirus from chest X-ray images. The hybrid deep learning model is a combination of ResNet50 and MobileNet. Both ResNet50 and MobileNet are light deep neural networks (DNNs) and can be used with low hardware resource-based personal digital assistants (PDA) for quick detection of COVID-19 infection.ResultsThe performance of the proposed hybrid model is evaluated on two publicly available COVID-19 chest X-ray datasets. Both datasets include normal, pneumonia, and coronavirus-infected chest X-rays and we achieve 84.35% and 94.43% accuracy on Dataset 1 and Dataset 2 respectively.ConclusionResults show that the proposed hybrid model is better suited for COVID-19 detection.

Journal

Research on Biomedical Engineering – Springer Journals

Published: Dec 1, 2021

Keywords: COVID-19 detection; MobileNet; ResNet50; Hybrid model; Pneumonia; X-rays

References

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