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

2020; Public Library of Science; Volume: 15; Issue: 6 Linguagem: Inglês

10.1371/journal.pone.0235187

1932-6203

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

Digital Imaging for Blood Diseases

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.