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

Journal: PloS one
Published Date: Jun 26, 2020

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.

Authors

  • Mohamed Abd Elaziz
    Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt.
  • Khalid M Hosny
    Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt.
  • Ahmad Salah
    Faculty of Computers and Informatics, Zagazig University, Sharkeya 44523, Egypt.
  • Mohamed M Darwish
    Faculty of Science, Assiut University, Assiut, Egypt.
  • Songfeng Lu
    School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China.
  • Ahmed T Sahlol
    Faculty of Specific Education, Damietta University, Damietta, Egypt.

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