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

Ritika Nandi, Manjunath Mulimani

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)687-695
Number of pages9
JournalResearch on Biomedical Engineering
Volume37
Issue number4
DOIs
Publication statusPublished - 12-2021

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

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