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  • WOANet: Whale Optimized Deep Neural Network for the Classification of COVID-19 from Radiography Images

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    Mirjalili519607-Accepted.pdf (1.685Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Murugan, R
    Goel, Tripti
    Mirjalili, Seyedali
    Chakrabartty, Deba Kumar
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2021
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    Abstract
    Coronavirus Diseases (COVID-19) is a new disease that will be declared a global pandemic in 2020. It is characterized by a constellation of traits like fever, dry cough, dyspnea, fatigue, chest pain, etc. Clinical findings have shown that the human chest Computed Tomography(CT) images can diagnose lung infection in most COVID-19 patients. Visual changes in CT scan due to COVID-19 is subjective and evaluated by radiologists for diagnosis purpose. Deep Learning (DL) can provide an automatic diagnosis tool to relieve radiologists' burden for quantitative analysis of CT scan images in patients. However, DL techniques face different ...
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    Coronavirus Diseases (COVID-19) is a new disease that will be declared a global pandemic in 2020. It is characterized by a constellation of traits like fever, dry cough, dyspnea, fatigue, chest pain, etc. Clinical findings have shown that the human chest Computed Tomography(CT) images can diagnose lung infection in most COVID-19 patients. Visual changes in CT scan due to COVID-19 is subjective and evaluated by radiologists for diagnosis purpose. Deep Learning (DL) can provide an automatic diagnosis tool to relieve radiologists' burden for quantitative analysis of CT scan images in patients. However, DL techniques face different training problems like mode collapse and instability. Deciding on training hyper-parameters to adjust the weight and biases of DL by a given CT image dataset is crucial for achieving the best accuracy. This paper combines the backpropagation algorithm and Whale Optimization Algorithm (WOA) to optimize such DL networks. Experimental results for the diagnosis of COVID-19 patients from a comprehensive COVID-CT scan dataset show the best performance compared to other recent methods. The proposed network architecture results were validated with the existing pre-trained network to prove the efficiency of the network.
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    Journal Title
    Biocybernetics and Biomedical Engineering
    DOI
    https://doi.org/10.1016/j.bbe.2021.10.004
    Copyright Statement
    © 2021 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Biomedical imaging
    COVID-19
    Deep Learning
    Early diagnosis
    Machine Learning
    Whale Optimization Algorithm
    Publication URI
    http://hdl.handle.net/10072/409929
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    • Journal articles

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