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

dc.creatorSolano Rojas, Braulio José
dc.creatorVillalón Fonseca, Ricardo
dc.creatorMarín Raventós, Gabriela
dc.date.accessioned2020-07-23T20:39:20Z
dc.date.accessioned2022-10-19T23:41:32Z
dc.date.available2020-07-23T20:39:20Z
dc.date.available2022-10-19T23:41:32Z
dc.date.created2020-07-23T20:39:20Z
dc.date.issued2020-06-23
dc.identifierhttps://link.springer.com/chapter/10.1007/978-3-030-51517-1_1
dc.identifier978-3-030-51517-1
dc.identifier978-3-030-51516-4
dc.identifierhttps://hdl.handle.net/10669/81345
dc.identifier10.1007/978-3-030-51517-1_1
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4522045
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.
dc.languageen_US
dc.sourceLecture Notes in Computer Science, Volúmen 12157, Año 2020, Editorial Springer.
dc.subjectAlzheimer
dc.subjectDeep learning
dc.subjectMRI
dc.subjectComputer-aided detection
dc.subjectComputer-aided diagnosis
dc.titleAlzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture
dc.typecontribución de congreso


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