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A radial basis function classifier for the automatic diagnosis of cerebral vascular accidents

2016Journal article Restricted access
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dc.contributor.authorRuano, M. Graça
dc.contributor.authorHajimani, Elmira
dc.contributor.authorRuano, Antonio
dc.date.accessioned2017-04-07T15:57:25Z
dc.date.available2017-04-07T15:57:25Z
dc.date.issued2016
dc.description.abstractA Radial Basis Function Neural Network (RBFNN) based diagnosis system for automatic identification of Cerebral Vascular Accident (CVA) through analysis of Computer Tomographic images (CT) is presented. For the design of a neural network classifier, most published methods just focus on the feature selection aspect and do not consider any approach for determining a model structure that best fits the application at their hand. Moreover, considering the domain of lesion detection from brain tissues, their feature space rarely contains symmetry/asymmetry information with respect to ideal mid-sagittal line. Another issue is how to handle multiple conflicting objectives in the design process, such as the maximization of both specificity and sensitivity, enforcing as well generalization. To deal with these challenges, a Multi Objective Genetic Algorithm (MOGA) based approach is used to determine the architecture of the classifier, its corresponding parameters and input features subject to multiple objectives, as well as their corresponding restrictions and priorities.
dc.description.sponsorshipSupport of H2020-EU.4.b., through CISUC, project 692023, FCT, through IDMEC, under LAETA
dc.identifier.isbn978-1-5090-2486-5
dc.identifier.otherAUT: ARU00698; MRU00118;
dc.identifier.urihttp://hdl.handle.net/10400.1/9706
dc.language.isoeng
dc.peerreviewedyes
dc.relation.isbasedonWOS:000386532100046
dc.titleA radial basis function classifier for the automatic diagnosis of cerebral vascular accidents
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEMS%2F50022%2F2013/PT
oaire.citation.conferencePlaceMadrid, Spain
oaire.citation.titleGlobal Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE), 2016
oaire.fundingStream5876
person.familyNameRuano
person.familyNameHajimani
person.familyNameRuano
person.givenNameMaria
person.givenNameElmira
person.givenNameAntonio
person.identifier.ciencia-id9811-A0DD-D5A5
person.identifier.orcid0000-0002-0014-9257
person.identifier.orcid0000-0003-4720-1590
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.rightsrestrictedAccess
rcaap.typearticle
relation.isAuthorOfPublication61fc8492-d73f-46ca-a3a3-4cd762a784e6
relation.isAuthorOfPublicationeb58b413-987f-4a74-97b4-2bf507777cb8
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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