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Automatic Detection of COVID-19 from Chest X-ray Images with Convolutional Neural Networks

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  • Additional Information
    • Publication Information:
      IEEE
    • Publication Date:
      2020
    • Abstract:
      Deep Learning has improved multi-fold in recent years and it has been playing a great role in image classification which also includes medical imaging. Convolutional Neural Networks (CNN) has been performing well in detecting many diseases including Coronary Artery Disease, Malaria, Alzheimer’s disease, different dental diseases, and Parkinson’s disease. Like other cases, CNN has a substantial prospect in detecting COVID-19 patients with medical images like chest X-rays and CTs. Coronavirus or COVID-19 has been declared a global pandemic by the World Health Organization (WHO). Till July 11, 2020, the total COVID-19 confirmed cases are 12.32 M and deaths are 0.556 M worldwide. Detecting Corona positive patients is very important in preventing the spread of this virus. On this conquest, a CNN model is proposed to detect COVID-19 patients from chest X-ray images. This model is evaluated with a comparative analysis of two other CNN models. The proposed model performs with an accuracy of 97.56% and a precision of 95.34%. This model gives the Receiver Operating Characteristic (ROC) curve area of 0.976 and F1-score of 97.61. It can be improved further by increasing the dataset for training the model.
    • Contents Note:
      Conference Acronym: iCCECE
    • Author Affiliations:
      Michigan University,College of Science and Engineering Central,Mt Pleasant,MI,USA
    • ISBN:
      978-1-7281-6330-7
      978-1-7281-6329-1
    • Relation:
      2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)
    • Accession Number:
      10.1109/iCCECE49321.2020.9231235
    • Rights:
      Copyright 2020, IEEE
    • AMSID:
      9231235
    • Conference Acronym:
      iCCECE
    • Date of Current Version:
      2020
    • Document Subtype:
      IEEE Conference
    • Notes:
      Conference Location: Southend, United Kingdom, United Kingdom

      Conference Start Date: 17 Aug. 2020

      Conference End Date: 18 Aug. 2020
    • Accession Number:
      edseee.9231235
  • Citations
    • ABNT:
      FOYSAL HAQUE, K. et al. Automatic Detection of COVID-19 from Chest X-ray Images with Convolutional Neural Networks. 2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE), Computing, Electronics & Communications Engineering (iCCECE), 2020 International Conference on, [s. l.], p. 125–130, 2020. DOI 10.1109/iCCECE49321.2020.9231235. Disponível em: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edseee&AN=edseee.9231235. Acesso em: 26 nov. 2020.
    • AMA:
      Foysal Haque K, Farhan Haque F, Gandy L, Abdelgawad A. Automatic Detection of COVID-19 from Chest X-ray Images with Convolutional Neural Networks. 2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE), Computing, Electronics & Communications Engineering (iCCECE), 2020 International Conference on. August 2020:125-130. doi:10.1109/iCCECE49321.2020.9231235
    • APA:
      Foysal Haque, K., Farhan Haque, F., Gandy, L., & Abdelgawad, A. (2020). Automatic Detection of COVID-19 from Chest X-ray Images with Convolutional Neural Networks. 2020 International Conference on Computing, Electronics & Communications Engineering (ICCECE), Computing, Electronics & Communications Engineering (ICCECE), 2020 International Conference On, 125–130. https://doi.org/10.1109/iCCECE49321.2020.9231235
    • Chicago/Turabian: Author-Date:
      Foysal Haque, Khandaker, Fatin Farhan Haque, Lisa Gandy, and Ahmed Abdelgawad. 2020. “Automatic Detection of COVID-19 from Chest X-Ray Images with Convolutional Neural Networks.” 2020 International Conference on Computing, Electronics & Communications Engineering (ICCECE), Computing, Electronics & Communications Engineering (ICCECE), 2020 International Conference On, August, 125–30. doi:10.1109/iCCECE49321.2020.9231235.
    • Harvard:
      Foysal Haque, K. et al. (2020) ‘Automatic Detection of COVID-19 from Chest X-ray Images with Convolutional Neural Networks’, 2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE), Computing, Electronics & Communications Engineering (iCCECE), 2020 International Conference on, pp. 125–130. doi: 10.1109/iCCECE49321.2020.9231235.
    • Harvard: Australian:
      Foysal Haque, K, Farhan Haque, F, Gandy, L & Abdelgawad, A 2020, ‘Automatic Detection of COVID-19 from Chest X-ray Images with Convolutional Neural Networks’, 2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE), Computing, Electronics & Communications Engineering (iCCECE), 2020 International Conference on, pp. 125–130, viewed 26 November 2020, .
    • MLA:
      Foysal Haque, Khandaker, et al. “Automatic Detection of COVID-19 from Chest X-Ray Images with Convolutional Neural Networks.” 2020 International Conference on Computing, Electronics & Communications Engineering (ICCECE), Computing, Electronics & Communications Engineering (ICCECE), 2020 International Conference On, Aug. 2020, pp. 125–130. EBSCOhost, doi:10.1109/iCCECE49321.2020.9231235.
    • Chicago/Turabian: Humanities:
      Foysal Haque, Khandaker, Fatin Farhan Haque, Lisa Gandy, and Ahmed Abdelgawad. “Automatic Detection of COVID-19 from Chest X-Ray Images with Convolutional Neural Networks.” 2020 International Conference on Computing, Electronics & Communications Engineering (ICCECE), Computing, Electronics & Communications Engineering (ICCECE), 2020 International Conference On, August 17, 2020, 125–30. doi:10.1109/iCCECE49321.2020.9231235.
    • Vancouver/ICMJE:
      Foysal Haque K, Farhan Haque F, Gandy L, Abdelgawad A. Automatic Detection of COVID-19 from Chest X-ray Images with Convolutional Neural Networks. 2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE), Computing, Electronics & Communications Engineering (iCCECE), 2020 International Conference on [Internet]. 2020 Aug 17 [cited 2020 Nov 26];125–30. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edseee&AN=edseee.9231235