Automatic classification of dyslexic children by applying machine learning to fMRI images

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dc.contributor.authorGarcía Chimeno, Yolanda
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorSaralegui Prieto, Ibone
dc.contributor.author Fernández-Ruanova, Begoña
dc.date.accessioned2026-09-21T17:53:33Z
dc.date.available2026-09-21T17:53:33Z
dc.date.issued2014-11-01
dc.date.updated2026-09-21T17:53:33Z
dc.descriptionPonencia presentada en la 3rd International Conference on Biomedical Engineering and Biotechnology, celebrada en Beijing, China entre el 25 y el 28 de septiembre de 2014en
dc.description.abstractFunctional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI) are a source of information to study different pathologies. This tool allows to classify subjects under study, analysing in this case, the functions related to language in young patients with dyslexia. Images are obtained using a scanner and different tests are performed on subjects. After processing the images, the areas that are activated by patients when performing the paradigms or anatomy of the tracts were obtained. The main objective is to ultimately introduce a group of monocular vision subjects, whose brain activation model is unknown. This classification helps to assess whether these subjects are more akin to dyslexic or control subjects. Machine learning techniques study systems that learn how to perform non-linear classifications through supervised or unsupervised training, or a combination of both. Once the machine has been set up, it is validated with the subjects who have not been entered in the training stage. The results are obtained using a user-friendly chart. Finally, a new tool for the classification of subjects with dyslexia and monocular vision was obtained (achieving a success rate of 94.8718% on the Neuronal Network classifier), which can be extended to other further classifications.en
dc.description.sponsorshipThis publication has been founding by eVIDA research group grant from Education and Researchdepartment of the Basque Country, Deiker from University of Deustoen
dc.identifier.citationChimeno, Y. G., Zapirain, B. G., Prieto, I. S., & Fernandez-Ruanova, B. (2014). Automatic classification of dyslexic children by applying machine learning to fMRI images. Bio-Medical Materials and Engineering, 24(6), 2995-3002. https://doi.org/10.3233/BME-141120
dc.identifier.doi10.3233/BME-141120
dc.identifier.eissn1878-3619
dc.identifier.issn0959-2989
dc.identifier.urihttps://hdl.handle.net/20.500.14454/6671
dc.language.isoeng
dc.publisherIOS Press
dc.rights© 2014 – IOS Press and the authors
dc.subject.otherClassifier
dc.subject.otherDyslexic
dc.subject.otherFMRI
dc.subject.otherMonocular vision
dc.subject.otherPCA
dc.titleAutomatic classification of dyslexic children by applying machine learning to fMRI imagesen
dc.typeconference paper
dcterms.accessRightsopen access
oaire.citation.endPage3002
oaire.citation.issue6
oaire.citation.startPage2995
oaire.citation.titleBio-Medical Materials and Engineering
oaire.citation.volume24
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc/4.0/
oaire.versionVoR
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