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

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 (m...

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Autores principales: Solano-Rojas, Braulio, Villalón-Fonseca, Ricardo, Marín-Raventós, Gabriela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7313295/
http://dx.doi.org/10.1007/978-3-030-51517-1_1
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author Solano-Rojas, Braulio
Villalón-Fonseca, Ricardo
Marín-Raventós, Gabriela
author_facet Solano-Rojas, Braulio
Villalón-Fonseca, Ricardo
Marín-Raventós, Gabriela
author_sort Solano-Rojas, Braulio
collection PubMed
description 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.
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spelling pubmed-73132952020-06-24 Alzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture Solano-Rojas, Braulio Villalón-Fonseca, Ricardo Marín-Raventós, Gabriela The Impact of Digital Technologies on Public Health in Developed and Developing Countries Article 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. 2020-05-31 /pmc/articles/PMC7313295/ http://dx.doi.org/10.1007/978-3-030-51517-1_1 Text en © The Author(s) 2020 Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
spellingShingle Article
Solano-Rojas, Braulio
Villalón-Fonseca, Ricardo
Marín-Raventós, Gabriela
Alzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture
title Alzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture
title_full Alzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture
title_fullStr Alzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture
title_full_unstemmed Alzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture
title_short Alzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture
title_sort alzheimer’s disease early detection using a low cost three-dimensional densenet-121 architecture
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7313295/
http://dx.doi.org/10.1007/978-3-030-51517-1_1
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