About: Classification of COVID-19 patients from chest CT images using multi-objective differential evolution

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About: Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks Goto Sponge NotDistinct Permalink

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	Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks
Creator	Kaur, Manjit Kumar, Vijay Singh, Dilbag Vaishali, &
Source	Medline; PMC
abstract	Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals’ precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (−ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.
has issue date	2020-04-27(xsd:dateTime)
bibo:doi	10.1007/s10096-020-03901-z
bibo:pmid	32337662
has license	no-cc
sha1sum (hex)	fee7feaf68010a92153aaf87123be05375ebd33c
schema:url	https://doi.org/10.1007/s10096-020-03901-z
resource representing a document's title	Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks
has PubMed Central identifier	PMC7183816
has PubMed identifier	32337662
schema:publication	Eur J Clin Microbiol Infect Dis
resource representing a document's body	covid:fee7feaf68010a92153aaf87123be05375ebd33c#body_text
is schema:about of	named entity 'Almost' named entity 'COVID-19' named entity 'radiology' named entity 'rapid' named entity 'differential evolution' named entity 'IMAGES' named entity 'EPIDEMIC' named entity 'CLASSIFICATION' named entity 'CHEST COMPUTED TOMOGRAPHY' named entity 'CT imaging' named entity 'chest' named entity 'CNN' named entity 'region' named entity 'coronavirus' named entity 'control' named entity 'MODE' named entity 'disease' named entity 'differential evolution' named entity 'CNN' named entity 'CNN' named entity 'multiobjective' named entity 'chest CT' named entity 'convolutional neural networks' named entity 'infection' named entity 'machine learning techniques' named entity 'COVID' named entity 'precious time' named entity 'classify' named entity 'nCoV' named entity 'infection' named entity 'COVID' named entity 'chest CT' named entity 'nCoV' named entity 'COVID' named entity 'convolution operator' named entity 'coronavirus' named entity 'Darwin' named entity 'vector' named entity 'X-ray images' named entity 'natural selection' named entity 'fuzzy inference systems' named entity 'cough' named entity 'COVID' named entity 'fatigue' named entity 'nCoV' named entity 'Spain' named entity 'CNN' named entity 'Chinese Government' named entity 'ANFIS' named entity 'convolutional neural networks' named entity 'differential evolution' named entity 'fitness function' named entity 'chest CT' named entity 'confusion matrix' named entity 'chest CT' named entity 'vector' named entity 'COVID' named entity 'CNN' named entity 'chest CT' named entity 'COVID' named entity 'vectors' named entity 'China' named entity 'CNN' named entity 'chest CT' named entity '1, 2' named entity 'ground-glass opacities' named entity 'mutation' named entity 'viral pneumonia' named entity 'convolutional neural networks' named entity 'MODE-based' named entity 'CNN' named entity 'nCoV' named entity 'deep learning' named entity 'CNN' named entity 'CNN' named entity 'COVID' named entity 'COVID' named entity 'infection'

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