An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19.

Journal: Computational and mathematical methods in medicine
PMID: 34194535

Abstract

Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant's technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.

Authors

  • Woan Ching Serena Low
    Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 40603 Kuala Lumpur, Malaysia.
  • Joon Huang Chuah
    Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 40603 Kuala Lumpur, Malaysia.
  • Clarence Augustine T H Tee
    Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 40603 Kuala Lumpur, Malaysia.
  • Shazia Anis
    Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 40603 Kuala Lumpur, Malaysia.
  • Muhammad Ali Shoaib
    Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 40603 Kuala Lumpur, Malaysia.
  • Amir Faisal
    Department of Biomedical Engineering, Faculty of Production and Industrial Technology, Institut Teknologi Sumatera, Lampung 35365, Indonesia.
  • Azira Khalil
    Faculty of Science and Technology, Islamic Science University of Malaysia, 71800, Nilai, Negeri Sembilan, Malaysia.
  • Khin Wee Lai
    Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia.

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