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An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images

2017-07Journal article Open access
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dc.contributor.authorHajimani, Elmira
dc.contributor.authorRuano, M G
dc.contributor.authorRuano, Antonio
dc.date.accessioned2019-11-20T15:07:30Z
dc.date.available2019-11-20T15:07:30Z
dc.date.issued2017-07
dc.description.abstractObjective: This paper presents a Radial Basis Functions Neural Network (RBFNN) based detection system, for automatic identification of Cerebral Vascular Accidents (CVA) through analysis of Computed Tomographic (CT) images. Methods: For the design of a neural network classifier, a Multi Objective Genetic Algorithm (MOGA) framework is used to determine the architecture of the classifier, its corresponding parameters and input features by maximizing the classification precision, while ensuring generalization. This approach considers a large number of input features, comprising first and second order pixel intensity statistics, as well as symmetry/asymmetry information with respect to the ideal mid-sagittal line. Results: Values of specificity of 98% and sensitivity of 98% were obtained, at pixel level, by an ensemble of non-dominated models generated by MOGA, in a set of 150 CT slices (1,867,602 pixels), marked by a NeuroRadiologist. This approach also compares favorably at a lesion level with three other published solutions, in terms of specificity (86% compared with 84%), degree of coincidence of marked lesions (89% compared with 77%) and classification accuracy rate (96% compared with 88%). (C) 2017 Published by Elsevier Ireland Ltd.
dc.description.sponsorshipFCT
dc.description.sponsorshipIDMEC
dc.description.sponsorshipLAETA [UID/EMS/50022/2013]
dc.identifier.doi10.1016/j.cmpb.2017.05.005
dc.identifier.issn0169-2607
dc.identifier.issn1872-7565
dc.identifier.urihttp://hdl.handle.net/10400.1/13074
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.subjectMultiple-sclerosis lesions
dc.subjectSegmentation
dc.subjectNetworks
dc.subjectClassification
dc.subjectAlgorithm
dc.subjectFeatures
dc.subjectModels
dc.titleAn intelligent support system for automatic detection of cerebral vascular accidents from brain CT images
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEMS%2F50022%2F2013/PT
oaire.citation.endPage123
oaire.citation.startPage109
oaire.citation.titleComputer Methods and Programs in Biomedicine
oaire.citation.volume146
oaire.fundingStream5876
person.familyNameHajimani
person.familyNameRuano
person.familyNameRuano
person.givenNameElmira
person.givenNameMaria
person.givenNameAntonio
person.identifier.ciencia-id9811-A0DD-D5A5
person.identifier.orcid0000-0003-4720-1590
person.identifier.orcid0000-0002-0014-9257
person.identifier.orcid0000-0002-6308-8666
person.identifier.ridA-8321-2011
person.identifier.ridB-4135-2008
person.identifier.scopus-author-id7004483805
person.identifier.scopus-author-id7004284159
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
rcaap.typearticle
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relation.isAuthorOfPublication61fc8492-d73f-46ca-a3a3-4cd762a784e6
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication.latestForDiscovery61fc8492-d73f-46ca-a3a3-4cd762a784e6
relation.isProjectOfPublication53083a92-791c-473a-8e29-3007fc4bb131
relation.isProjectOfPublication.latestForDiscovery53083a92-791c-473a-8e29-3007fc4bb131

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