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Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches pp 63–79Cite as

Automatic Scoring and Grading of COVID-19 Lung Infection Approach

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Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 322))

Abstract

Although the successful detection of COVID-19 from lung computed tomography (CT) image mainly depends on radiologist’s experience, specialists occasionally disagree with their judgments. The performance of COVID-19 detection models needs to be improved. According to COVID-19 symptoms and human immune approach response, there are four types of its contagion such as asymptomatic, mild, severe, and recovered. In this chapter, an automatic scoring of COVID-19 lung infection grading approach is presented. The proposed approach is based on a combination of image segmentation techniques and the Particle Swarm Optimization (PSO) algorithm to access accurate evaluation for infection rate. Fuzzy c-means, K-means and thresholding-based segmentation algorithms are used for isolating the chest lung from the CT images. Then, PSO is used with the three segmentation algorithms for clustering the region of interest (ROI) that consists of COVID-19 infected regions in lung CT. Then, scoring the infection rate for each case. Finally, four infection classes related to the obtained infection COVID-19 is determined and classified.

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Author information

Authors and Affiliations

  1. Center for Virus Research and Studies, Al-Azhar University, Cairo, Egypt

    Kamel. K. Mohammed

  2. System and Biomedical Engineering Department, Higher Institute of Engineering in El-Shorouk City, Cairo, Egypt

    Heba M. Afify

  3. Faculty of Science, Helwan Uinversity, Helwan, Egypt

    Ashraf Darwish

  4. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  5. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Kamel. K. Mohammed, Heba M. Afify, Ashraf Darwish & Aboul Ella Hassanien

Authors
  1. Kamel. K. Mohammed
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  2. Heba M. Afify
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  3. Ashraf Darwish
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  4. Aboul Ella Hassanien
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Corresponding author

Correspondence to Kamel. K. Mohammed .

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Editors and Affiliations

  1. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwish

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Mohammed, K.K., Afify, H.M., Darwish, A., Hassanien, A.E. (2021). Automatic Scoring and Grading of COVID-19 Lung Infection Approach. In: Hassanien, A.E., Darwish, A. (eds) Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches. Studies in Systems, Decision and Control, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-030-63307-3_4

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