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Improving the diagnosis of ischemic CVA's through CT scan with neural networks

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dc.contributor.authorRibeiro, Luís
dc.contributor.authorRuano, Antonio
dc.contributor.authorRuano, M. Graça
dc.contributor.authorFerreira, P. M.
dc.contributor.authorVarkonyi-Koczy, A. R.
dc.date.accessioned2013-02-07T15:13:01Z
dc.date.available2013-02-07T15:13:01Z
dc.date.issued2007
dc.date.updated2013-01-26T18:17:30Z
dc.description.abstractTechnological and computing evolution promoted new opportunities to improve the quality of life, in particular, the quality of diagnostic evaluations. Computerized tomography is one of the imaging equipments of diagnosis which has most benefited from technological improvements. Because of that, and due to the quality of the diagnosis produced, it is one of the most employed equipments in clinical applications. The ischaemic cerebral vascular accident (ICVA) is the pathology that confirms the frequent use of the computerized tomography. The interest for this pathology, and in general for the encephalon image analysis as a preventive diagnosis, is mainly due to the frequent occurrence of ICVAs in development countries and its social-economic impact. In this sense, we propose to evaluate the ability of artificial neural networks (ANN) for automatic identification of ICVA by means of tissue density images obtained by computerised tomography. This work employed cranioencephalon computerised tomography exams and their respective medical reports, to train ANNs classifiers. Features extracted from the images were used as inputs to the classifiers. Once the ANNs were trained, the neural classifiers were tested with data never seen by the network. At this stage we may conclude that the ANNs may significantly contribute as an ICVAs computerised tomography diagnostic aid, since among the test cases the automatic identification of ischaemic lesions has been performed with no false negatives e very few false positives.por
dc.identifier.citationRibeiro, Luis; Ruano, Antonio E. B.; Ruano, M. Graca; Ferreira, Pedro; Varkonyi-Koczy, Annamaria R. Improving the Diagnosis of Ischemic CVA's through CT Scan with Neural Networks, Trabalho apresentado em 2007 2nd International Workshop on Soft Computing Applications, In Proceedings of the 2007 2nd International Workshop on Soft Computing Applications, Gyula, Hungary, 2007.por
dc.identifier.doihttp://dx.doi.org/10.1109/SOFA.2007.4318302
dc.identifier.isbn978-1-4244-1607-3
dc.identifier.otherAUT: ARU00698; MRU00118; LPR02149;
dc.identifier.urihttp://hdl.handle.net/10400.1/2256
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherIEEEpor
dc.titleImproving the diagnosis of ischemic CVA's through CT scan with neural networkspor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceGyula, Hungarypor
oaire.citation.endPage43por
oaire.citation.startPage39por
oaire.citation.title2nd IEEE International Workshop on Soft Computing Applications (SOFA 2007)por
person.familyNameRibeiro
person.familyNameRuano
person.familyNameRuano
person.givenNameLuís Pedro
person.givenNameAntonio
person.givenNameMaria
person.identifier2176242
person.identifier.ciencia-idC815-8A07-1950
person.identifier.ciencia-id9811-A0DD-D5A5
person.identifier.orcid0000-0002-6967-0534
person.identifier.orcid0000-0002-6308-8666
person.identifier.orcid0000-0002-0014-9257
person.identifier.ridA-6697-2016
person.identifier.ridB-4135-2008
person.identifier.ridA-8321-2011
person.identifier.scopus-author-id58401765500
person.identifier.scopus-author-id7004284159
person.identifier.scopus-author-id7004483805
rcaap.rightsrestrictedAccesspor
rcaap.typeconferenceObjectpor
relation.isAuthorOfPublication34d8529e-10eb-468a-b4a4-f652d90be308
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication61fc8492-d73f-46ca-a3a3-4cd762a784e6
relation.isAuthorOfPublication.latestForDiscovery34d8529e-10eb-468a-b4a4-f652d90be308

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