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  • Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning

    Author(s)
    Zheng, F
    Li, L
    Zhang, X
    Song, Y
    Huang, Z
    Chong, Y
    Chen, Z
    Zhu, H
    Wu, J
    Chen, W
    Lu, Y
    Yang, Y
    Zha, Y
    Zhao, H
    Shen, J
    Griffith University Author(s)
    Yang, Yuedong
    Year published
    2021
    Metadata
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    Abstract
    Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films brings a lot of concentration and time for doctors. Therefore, it is necessary to develop an automatic CT image diagnosis system to assist doctors in diagnosis. Previous studies devoted to COVID-19 in the past months focused mostly on discriminating COVID-19 infected patients from healthy persons and/or bacterial pneumonia patients, and have ignored typical viral pneumonia since it is hard to collect samples for viral pneumonia that is less frequent in adults. In addition, it is ...
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    Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films brings a lot of concentration and time for doctors. Therefore, it is necessary to develop an automatic CT image diagnosis system to assist doctors in diagnosis. Previous studies devoted to COVID-19 in the past months focused mostly on discriminating COVID-19 infected patients from healthy persons and/or bacterial pneumonia patients, and have ignored typical viral pneumonia since it is hard to collect samples for viral pneumonia that is less frequent in adults. In addition, it is much more challenging to discriminate COVID-19 from typical viral pneumonia as COVID-19 is also a kind of virus. In this study, we have collected CT images of 262, 100, 219, and 78 persons for COVID-19, bacterial pneumonia, typical viral pneumonia, and healthy controls, respectively. To the best of our knowledge, this was the first study of quaternary classification to include also typical viral pneumonia. To effectively capture the subtle differences in CT images, we have constructed a new model by combining the ResNet50 backbone with SE blocks that was recently developed for fine image analysis. Our model was shown to outperform commonly used baseline models, achieving an overall accuracy of 0.94 with AUC of 0.96, recall of 0.94, precision of 0.95, and F1-score of 0.94. The model is available in https://github.com/Zhengfudan/COVID-19-Diagnosis-and-Pneumonia-Classification.
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    Journal Title
    Interdisciplinary Sciences: Computational Life Sciences
    DOI
    https://doi.org/10.1007/s12539-021-00420-z
    Note
    This publication has been entered in Griffith Research Online as an advanced online version.
    Subject
    Clinical Sciences
    Biological Sciences
    Medical Microbiology
    Publication URI
    http://hdl.handle.net/10072/403336
    Collection
    • Journal articles

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