| dc.creator | Solano Rojas, Braulio José | |
| dc.creator | Villalón Fonseca, Ricardo | |
| dc.creator | Marín Raventós, Gabriela | |
| dc.date.accessioned | 2020-07-23T20:39:20Z | |
| dc.date.available | 2020-07-23T20:39:20Z | |
| dc.date.issued | 2020-06-23 | |
| dc.identifier.citation | https://link.springer.com/chapter/10.1007/978-3-030-51517-1_1 | |
| dc.identifier.isbn | 978-3-030-51517-1 | |
| dc.identifier.isbn | 978-3-030-51516-4 | |
| dc.identifier.uri | https://hdl.handle.net/10669/81345 | |
| dc.description.abstract | The 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.language.iso | en_US | es_ES |
| dc.source | Lecture Notes in Computer Science, Volúmen 12157, Año 2020, Editorial Springer. | es_ES |
| dc.subject | Alzheimer | es_ES |
| dc.subject | Deep learning | es_ES |
| dc.subject | MRI | es_ES |
| dc.subject | Computer-aided detection | es_ES |
| dc.subject | Computer-aided diagnosis | es_ES |
| dc.title | Alzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture | es_ES |
| dc.type | contribución de congreso | |
| dc.identifier.doi | 10.1007/978-3-030-51517-1_1 | |
| dc.description.procedence | UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ingeniería::Centro de Investigaciones en Tecnologías de Información y Comunicación (CITIC) | es_ES |
| dc.description.procedence | UCR::Vicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ciencias de la Computación e Informática | es_ES |
