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  • Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network

    Author(s)
    Goel, Tripti
    Murugan, R
    Mirjalili, Seyedali
    Chakrabartty, Deba Kumar
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2021
    Metadata
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    Abstract
    The quick spread of coronavirus disease (COVID-19) has resulted in a global pandemic and more than fifteen million confirmed cases. To battle this spread, clinical imaging techniques, for example, computed tomography (CT), can be utilized for diagnosis. Automatic identification software tools are essential for helping to screen COVID-19 using CT images. However, there are few datasets available, making it difficult to train deep learning (DL) networks. To address this issue, a generative adversarial network (GAN) is proposed in this work to generate more CT images. The Whale Optimization Algorithm (WOA) is used to optimize ...
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    The quick spread of coronavirus disease (COVID-19) has resulted in a global pandemic and more than fifteen million confirmed cases. To battle this spread, clinical imaging techniques, for example, computed tomography (CT), can be utilized for diagnosis. Automatic identification software tools are essential for helping to screen COVID-19 using CT images. However, there are few datasets available, making it difficult to train deep learning (DL) networks. To address this issue, a generative adversarial network (GAN) is proposed in this work to generate more CT images. The Whale Optimization Algorithm (WOA) is used to optimize the hyperparameters of GAN's generator. The proposed method is tested and validated with different classification and meta-heuristics algorithms using the SARS-CoV-2 CT-Scan dataset, consisting of COVID-19 and non-COVID-19 images. The performance metrics of the proposed optimized model, including accuracy (99.22%), sensitivity (99.78%), specificity (97.78%), F1-score (98.79%), positive predictive value (97.82%), and negative predictive value (99.77%), as well as its confusion matrix and receiver operating characteristic (ROC) curves, indicate that it performs better than state-of-the-art methods. This proposed model will help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks.
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    Journal Title
    Cognit Comput
    DOI
    https://doi.org/10.1007/s12559-020-09785-7
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Artificial Intelligence and Image Processing
    Neurosciences
    Cognitive Sciences
    Automatic diagnosis
    COVID-19
    Coronavirus
    Deep learning
    Generative Adversarial Network
    Publication URI
    http://hdl.handle.net/10072/402109
    Collection
    • Journal articles

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