Selected article for: "accuracy achieve and low resource"
Author: Nandi, Ritika; Mulimani, Manjunath
Title: Detection of COVID-19 from X-rays using hybrid deep learning models
  • Cord-id: sfhjrv53
  • Document date: 2021_9_21
    • ID: sfhjrv53
    Snippet: 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
    Document: 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.

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