Medical aid system for Alzheimer's diagnosis using machine learning

dc.contributor.advisorTFEPaternain Dallo, Daniel
dc.contributor.advisorTFEGalar Idoate, Mikel
dc.contributor.affiliationEscuela Técnica Superior de Ingeniería Industrial, Informática y de Telecomunicaciónes_ES
dc.contributor.affiliationIndustria, Informatika eta Telekomunikazio Ingeniaritzako Goi Mailako Eskola Teknikoaeu
dc.contributor.authorUrtasun Urra, Jon
dc.date.accessioned2021-02-12T14:43:35Z
dc.date.available2021-02-12T14:43:35Z
dc.date.issued2021
dc.date.updated2021-02-11T10:52:48Z
dc.description.abstractArtificial Intelligence (AI), and precisely Machine Learning (ML), has been proved to be a considerably useful method in a wide variety of fields. One of the fields that has improved more is the computer vision. Computers can now apply ML techniques in order to solve tasks, such as image classification, which were inconceivable in the past. This led into a huge impact on the medicine field, which has increasingly ameliorated over the recent years. The purpose of this project is to prove whether ML techniques are able to successfully diagnose Alzheimer’s disease or not.en
dc.description.degreeGraduado o Graduada en Ingeniería Informática por la Universidad Pública de Navarraes_ES
dc.description.degreeInformatika Ingeniaritzako Graduatua Nafarroako Unibertsitate Publikoaneu
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/39189
dc.language.isoengen
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.subjectAlzheimeren
dc.subjectMachine learningen
dc.subjectMRIen
dc.subjectDataseten
dc.subjectConvolutional neural networken
dc.subjectSVMen
dc.titleMedical aid system for Alzheimer's diagnosis using machine learningen
dc.typeinfo:eu-repo/semantics/bachelorThesis
dspace.entity.typePublication
relation.isAdvisorTFEOfPublicationca16c024-51e4-4f8f-b457-dc5307be32d9
relation.isAdvisorTFEOfPublication44c7a308-9c21-49ef-aa03-b45c2c5a06fd
relation.isAdvisorTFEOfPublication.latestForDiscoveryca16c024-51e4-4f8f-b457-dc5307be32d9

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