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Binary Classification of COVID-19 CT Images Using CNN: COVID Diagnosis Using CT

Author

Listed:
  • Shankar Shambhu

    (Chitkara University School of Computer Applications, Chitkara University, Himachal Pradesh, India)

  • Deepika Koundal

    (Department of Vitualization, School of Computer Science, Universit of Petroleum and Energy Studies, Dehradun, India)

  • Prasenjit Das

    (Chitkara University School of Computer Applications, Chitkara University, Himachal Pradesh, India)

  • Chetan Sharma

    (Chitkara University, Himachal Pradesh, India)

Abstract

COVID-19 pandemic has hit the world with such a force that the world's leading economies are finding it challenging to come out of it. Countries with the best medical facilities are even cannot handle the increasing number of cases and fatalities. This disease causes significant damage to the lungs and respiratory system of humans, leading to their death. Computed tomography (CT) images of the respiratory system are analyzed in the proposed work to classify the infected people with non-infected people. Deep learning binary classification algorithms have been applied, which have shown an accuracy of 86.9% on 746 CT images of chest having COVID-19 related symptoms.

Suggested Citation

  • Shankar Shambhu & Deepika Koundal & Prasenjit Das & Chetan Sharma, 2021. "Binary Classification of COVID-19 CT Images Using CNN: COVID Diagnosis Using CT," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 13(2), pages 1-13, July.
    • Handle: RePEc:igg:jehmc0:v:13:y:2021:i:2:p:1-13
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    References listed on IDEAS

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