Alzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture
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| Autores: | , , |
|---|---|
| Formato: | contribución de congreso |
| Fecha de Publicación: | 2020 |
| Descripción: | 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. |
| País: | Kérwá |
| Institución: | Universidad de Costa Rica |
| Repositorio: | Kérwá |
| OAI Identifier: | oai:https://www.kerwa.ucr.ac.cr:10669/81345 |
| Acceso en línea: | https://link.springer.com/chapter/10.1007/978-3-030-51517-1_1 https://hdl.handle.net/10669/81345 |
| Access Level: | acceso abierto |
| Palabra clave: | Alzheimer Deep learning MRI Computer-aided detection Computer-aided diagnosis |