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

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	New machine learning method for image-based diagnosis of COVID-19
Creator	Abd Elaziz, Mohamed Darwish, Mohamed Hosnyid, Khalid Lu, Songfeng Sahlol, Ahmed Salah, Ahmad
Source	Medline; PMC
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.
has issue date	2020-06-26(xsd:dateTime)
bibo:doi	10.1371/journal.pone.0235187
bibo:pmid	32589673
has license	cc-by
sha1sum (hex)	59567457a5b9e01dd9794bb629787b4f93c31a5f
schema:url	https://doi.org/10.1371/journal.pone.0235187
resource representing a document's title	New machine learning method for image-based diagnosis of COVID-19
has PubMed Central identifier	PMC7319603
has PubMed identifier	32589673
schema:publication	PLoS One
resource representing a document's body	covid:59567457a5b9e01dd9794bb629787b4f93c31a5f#body_text
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