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

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.

Tipo de publicación: Conference Paper

Publicado en: Lecture Notes in Computer Science

Autores
  • Braulio Solano-Rojas
  • Ricardo Villalón-Fonseca
  • Gabriela Marín-Raventós

Investigadores del CITIC asociados a la publicación
Mag. Braulio Solano Rojas
Dr. Ricardo Villalón Fonseca
Dra. Gabriela Marín Raventós

Proyecto asociado a la publicación
Evaluación de algoritmos de computación evolutiva para el entrenamiento de redes de neuronas artificiales, aplicada a la detección temprana de Alzheimer

DOI BIBTEXT

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Cita bibliográfica
Alzheimer's Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture