A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer’s Disease Early Discovery


Alzheimer’s disease is the most prevalent dementia among the elderly population. Early detection is critical because it can help with future planning for those potentially affected. This paper uses a three-dimensional DenseNet architecture to detect Alzheimer’s disease in magnetic resonance imaging. Our work is restricted to the use of freely available tools. We constructed a deep neural network classifier with metrics of 0.86 mean accuracy, 0.86 mean sensitivity (micro-average), 0.86 mean specificity (micro-average), and 0.91 area under the receiver operating characteristic curve (micro-average) for the task of discriminating between five different disease stages or classes. The use of tools available for free ensures the reproducibility of the study and the applicability of the classification system in developing countries.

Tipo de publicación: Journal Article

Publicado en: Sensors

  • Braulio Solano-Rojas
  • Ricardo Villalón-Fonseca

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

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


Datos bibliográficos
Cita bibliográfica
A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer's Disease Early Discovery