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Alzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture

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dc.creatorSolano Rojas, Braulio José
dc.creatorVillalón Fonseca, Ricardo
dc.creatorMarín Raventós, Gabriela
dc.date.accessioned2020-07-23T20:39:20Z
dc.date.available2020-07-23T20:39:20Z
dc.date.issued2020-06-23
dc.description.abstractThe objective of this work is to detect Alzheimer’s disease using Magnetic Resonance Imaging. For this, we use a three-dimensional densenet-121 architecture. With the use of only freely available tools, we obtain good results: a deep neural network showing metrics of 87% accuracy, 87% sensitivity (micro-average), 88% specificity (micro-average), and 92% AUROC (micro-average) for the task of classifying five different classes (disease stages). The use of tools available for free means that this work can be replicated in developing countries.es_ES
dc.description.procedenceUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ingeniería::Centro de Investigaciones en Tecnologías de Información y Comunicación (CITIC)es_ES
dc.description.procedenceUCR::Vicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ciencias de la Computación e Informáticaes_ES
dc.identifier.citationhttps://link.springer.com/chapter/10.1007/978-3-030-51517-1_1
dc.identifier.doi10.1007/978-3-030-51517-1_1
dc.identifier.isbn978-3-030-51517-1
dc.identifier.isbn978-3-030-51516-4
dc.identifier.urihttps://hdl.handle.net/10669/81345
dc.language.isoen_USes_ES
dc.rightsacceso abierto
dc.sourceLecture Notes in Computer Science, Volúmen 12157, Año 2020, Editorial Springer.es_ES
dc.subjectAlzheimeres_ES
dc.subjectDeep learninges_ES
dc.subjectMRIes_ES
dc.subjectComputer-aided detectiones_ES
dc.subjectComputer-aided diagnosises_ES
dc.titleAlzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecturees_ES
dc.typecontribución de congreso

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