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

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

PLOS ONE, 2020, vol. 15, issue 6, 1-18

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0235187

DOI: 10.1371/journal.pone.0235187

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