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Automated Segmentation of COVID-19 Lesion from Lung CT Images Using U-Net Architecture

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Science and Technologies for Smart Cities (SmartCity360° 2020)

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Abstract

Pneumonia caused by the novel Coronavirus Disease (COVID-19) is emerged as a global threat and considerably affected a large population globally irrespective of their age, race, and gender. Due to its rapidity and the infection rate, the World Health Organization (WHO) declared this disease as a pandemic. The proposed research work aims to develop an automated COVID-19 lesion segmentation system using the Convolutional Neural Network (CNN) architecture called the U-Net. The traditional U-Net scheme is employed to examine the COVID-19 infection present in the lung CT images. This scheme is implemented on the benchmark COVID-19 images existing in the literature (300 images) and the segmentation performance of the U-Net is confirmed by computing the essential performance measures using a relative assessment among the extracted lesion and the Ground-Truth (GT). The overall result attained with the proposed study confirms that, the U-Net scheme helps to get the better values for the performance values, such as Jaccard (>86%), Dice (>92%) and segmentation accuracy (>95%).

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Author information

Authors and Affiliations

  1. Faculty of Applied Computing and Technology, Noroff University College, Kristiansand, Norway

    Seifedine Kadry

  2. Artificial Intelligence Department, Research Center for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey

    Fadi Al-Turjman

  3. Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai, India

    V. Rajinikanth

Authors
  1. Seifedine Kadry
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  2. Fadi Al-Turjman
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  3. V. Rajinikanth
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Editor information

Editors and Affiliations

  1. School of Technology and Management, Escola Superior de Tecnologia e Gestão, VIANA DO CASTELO, Portugal

    Sara Paiva

  2. Polytechnic Institute of Viana do Castel, Viana do Castelo, Portugal

    Sérgio Ivan Lopes

  3. SIC Laboratory, INSEEC U, ECE Paris Graduate School of Engineering, PARIS, France

    Rafik Zitouni

  4. Vaagdevi College of Engineering, Telangana, India

    Nishu Gupta

  5. University of Minho, Guimarães, Portugal

    Sérgio F. Lopes

  6. Nagoya University, Nagoya, Japan

    Takuro Yonezawa

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Kadry, S., Al-Turjman, F., Rajinikanth, V. (2021). Automated Segmentation of COVID-19 Lesion from Lung CT Images Using U-Net Architecture. In: Paiva, S., Lopes, S.I., Zitouni, R., Gupta, N., Lopes, S.F., Yonezawa, T. (eds) Science and Technologies for Smart Cities. SmartCity360° 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 372. Springer, Cham. https://doi.org/10.1007/978-3-030-76063-2_2

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  • DOI: https://doi.org/10.1007/978-3-030-76063-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-76062-5

  • Online ISBN: 978-3-030-76063-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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