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

Nandi, Ritika and Mulimani, Manjunath (2021) Detection of COVID-19 from X-rays using hybrid deep learning models. Research on Biomedical Engineering, 37. pp. 687-695. ISSN 2446-4732

[img] PDF
14181.pdf - Published Version
Restricted to Registered users only

Download (2MB) | Request a copy

Abstract

Purpose To 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). Methods In 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. Results The 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. Conclusion Results show that the proposed hybrid model is better suited for COVID-19 detection.

Item Type: Article
Uncontrolled Keywords: COVID-19 detection · MobileNet · ResNet50 · Hybrid model · Pneumonia · X-rays
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Depositing User: MIT Library
Date Deposited: 07 Feb 2022 05:06
Last Modified: 07 Feb 2022 05:06
URI: http://eprints.manipal.edu/id/eprint/158188

Actions (login required)

View Item View Item