Open AccessNew machine learning method for image-based diagnosis of COVID-19
Author(s) -
Mohamed Abd Elaziz,
Khalid M. Hosny,
Ahmad Salah,
Mohamed M. Darwish,
Songfeng Lu,
Ahmed T. Sahlol
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0235187
Subject(s) - covid-19 , artificial intelligence , computer science , process (computing) , differential evolution , image (mathematics) , machine learning , pattern recognition (psychology) , image registration , medicine , pathology , disease , outbreak , infectious disease (medical specialty) , operating system
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.