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Classification of MR and CT images using genetic algorithms

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dc.contributor.authorDokur, Z.
dc.contributor.authorOlmez, T.
dc.contributor.authorYazgan, E.
dc.contributor.ituauthorÖlmez, Zümray
dc.date.accessioned2026-01-25T13:12:37Z
dc.date.issued2002-11-27
dc.description.abstractA modified restricted Coulomb energy (MoRCE) network trained by the genetic algorithm is presented. Each neuron of the network forms a closed region in the input space. The closed regions which are formed by the neurons overlap each other, like STAR. Genetic algorithms are used to improve the classification performances of the magnetic resonance (MR) and computer tomography (CT) images with minimized number of neurons. MoRCE is examined comparatively with multilayer perceptron (MLP), and restricted Coulomb energy (RCE). It is observed that MoRCE gives the best classification performance with less number of neurons after a short training time.
dc.description.urihttps://doi.org/10.1109/iembs.1998.747149
dc.description.urihttps://dx.doi.org/10.1109/iembs.1998.747149
dc.identifier.doi10.1109/iembs.1998.747149
dc.identifier.endpage1421
dc.identifier.openairedoi_dedup___::946a72f4eaea38bcaa3b42a937f5807d
dc.identifier.orcid0000-0001-7660-3236
dc.identifier.startpage1418
dc.identifier.urihttps://hdl.handle.net/11527/51507
dc.identifier.volume3
dc.publisherIEEE
dc.relation.ispartofProceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286)
dc.titleClassification of MR and CT images using genetic algorithms
dc.typeArticle
dspace.entity.typePublication
person.identifier.orcid0000-0001-7660-3236

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