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New machine learning method for image-based diagnosis of COVID-19

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  • Mohamed Abd Elaziz
  • Khalid M Hosny
  • Ahmad Salah
  • Mohamed M Darwish
  • Songfeng Lu
  • Ahmed T Sahlol

Abstract

COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively.

Suggested Citation

  • Mohamed Abd Elaziz & Khalid M Hosny & Ahmad Salah & Mohamed M Darwish & Songfeng Lu & Ahmed T Sahlol, 2020. "New machine learning method for image-based diagnosis of COVID-19," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-18, June.
    • Handle: RePEc:plo:pone00:0235187
    DOI: 10.1371/journal.pone.0235187
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    References listed on IDEAS

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    1. Wang, Xiang-yang & Li, Wei-yi & Yang, Hong-ying & Wang, Pei & Li, Yong-wei, 2015. "Quaternion polar complex exponential transform for invariant color image description," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 951-967.
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    Cited by:

    1. Saqib Ali Nawaz & Jingbing Li & Uzair Aslam Bhatti & Sibghat Ullah Bazai & Asmat Zafar & Mughair Aslam Bhatti & Anum Mehmood & Qurat ul Ain & Muhammad Usman Shoukat, 2021. "A hybrid approach to forecast the COVID-19 epidemic trend," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-16, October.
    2. Yingying Liao & Weiguo Zhao & Liying Wang, 2021. "Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers," Mathematics, MDPI, vol. 9(18), pages 1-38, September.
    3. Mario A Quiroz-Juárez & Armando Torres-Gómez & Irma Hoyo-Ulloa & Roberto de J León-Montiel & Alfred B U’Ren, 2021. "Identification of high-risk COVID-19 patients using machine learning," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-21, September.
    4. Mohamed Abd Elaziz & Laith Abualigah & Dalia Yousri & Diego Oliva & Mohammed A. A. Al-Qaness & Mohammad H. Nadimi-Shahraki & Ahmed A. Ewees & Songfeng Lu & Rehab Ali Ibrahim, 2021. "Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection," Mathematics, MDPI, vol. 9(21), pages 1-17, November.
    5. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).

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