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

• Published in: PloS one Vol. 15; no. 6; p. e0235187
• Main Authors: Elaziz, Mohamed Abd, Hosny, Khalid M, Salah, Ahmad, Darwish, Mohamed M, Lu, Songfeng, Sahlol, Ahmed T
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 26-06-2020; Public Library of Science (PLoS)
• Subjects: Accuracy; Adult; Aged; Aged, 80 and over; Algorithms; Betacoronavirus; Biology and Life Sciences; Chest; Classification; Computer aided medical diagnosis; Computer and Information Sciences; Computer applications; Coronavirus Infections - diagnostic imaging; Coronaviruses; COVID-19; Datasets; Diagnosis; Diagnostic imaging; Epidemics; Evolutionary computation; Feature extraction; Female; Health aspects; Humans; Identification methods; Image classification; Informatics; Learning algorithms; Machine Learning; Male; Medical diagnosis; Medical imaging; Medicine and Health Sciences; Methods; Microprocessors; Middle Aged; Multichannel; Optimization; Pandemics; Performance evaluation; Physical Sciences; Pneumonia, Viral - diagnostic imaging; Radiography, Thoracic; Research and Analysis Methods; SARS-CoV-2; Social Sciences; Software; Thorax - diagnostic imaging; X-Rays; Egypt; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0235187

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.