Deep learning for diagnosing Alzheimer disease through the analysis of MRI

Authors

  • 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

Keywords:

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

Abstract

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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Published

2026-07-27

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

LACCEI retains copyright of all published articles under the terms of its copyright transfer agreement. As the copyright holder, LACCEI distributes the articles to the public under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).

How to Cite

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