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    Detection and severity classification of COVID-19 in CT images using deep learning

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    Date
    2021
    Author
    Qiblawey Y.
    Tahir A.
    Chowdhury M.E.H.
    Khandakar A.
    Kiranyaz, Mustafa Serkan
    Rahman T.
    Ibtehaz N.
    Mahmud S.
    Al Maadeed S.
    Musharavati F.
    Ayari M.A.
    ...show more authors ...show less authors
    Metadata
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    Abstract
    Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder?Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.
    URI
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106889669&doi=10.3390%2fdiagnostics11050893&partnerID=40&md5=0ab469a968b59e23dd94fd7f37b0d880
    DOI/handle
    http://dx.doi.org/10.3390/diagnostics11050893
    http://hdl.handle.net/10576/30599
    Collections
    • Computer Science & Engineering [‎2483‎ items ]
    • COVID-19 Research [‎849‎ items ]
    • Electrical Engineering [‎2846‎ items ]

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