Deep learning for diagnosing Alzheimer disease through the analysis of MRI

Autores/as

  • Elena Acevedo INSTITUTO POLITECNICO NACIONAL, México
  • Dinora Orantes INSTITUTO POLITECNICO NACIONAL, México
  • Marco Acevedo INSTITUTO POLITECNICO NACIONAL, México

DOI:

https://doi.org/10.18687/LACCEI2026.1.1.670

Palabras clave:

Artificial Intelligence, Supervised learning, Deep learning, Diagnosis, Alzheimer.

Resumen

Alzheimer's disease is a progressive brain disorder that affects memory, reasoning ability, and, eventually, the ability to perform simple daily tasks. People diagnosed with this dementia have a life expectancy of up to twenty years from diagnosis. A deep learning-based approach is presented for the classification and diagnosis of Alzheimer's disease using magnetic resonance imaging (MRI) scans. The dataset was obtained from the Kaggle platform, and the metrics of accuracy, recall, and F1 score were applied. Each of these metrics showed a percentage close to 100%, resulting in an average accuracy of 99.52%.

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Publicado

2026-07-27

Número

Sección

Articles

Licencia

Licencia Creative Commons

Esta obra está bajo una Licencia Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional.

LACCEI conserva el copyright de todos los artículos publicados bajo los términos de su acuerdo de transferencia de copyright. Como titular del copyright, LACCEI distribuye los artículos al público bajo la Licencia Internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0 (CC BY-NC-SA 4.0).

Cómo citar

Acevedo, E., Orantes, D., & Acevedo, M. (2026). Deep learning for diagnosing Alzheimer disease through the analysis of MRI. LACCEI, 1(14). https://doi.org/10.18687/LACCEI2026.1.1.670