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Implementation of a 3D Convolutional Neural Network Predictive Model for the Transition from Mild Cognitive Impairment to Alzheimer’s Disease using Magnetic Resonance Imaging (MRI) Images

10.24050/reia.v21i42.1775
Published 2024-07-01
Articles

Abstract

Alzheimer’s disease is a neurological disorder that causes loss of autonomy and memory in people who suffer from it. Due to the increase in cases of this disease and the lack of accuracy of diagnostic tools, new tools capable of addressing this issue are being developed. The main objective of this research work is to implement a three-dimensional convolutional neural network model with AlexNet3D type base structure to predict the possible diagnosis of Alzheimer’s disease (AD). from the analysis of magnetic resonance images, using as an early stage mild cognitive impairment syndrome (MCI). The construction phases of the project will be explained, divided into database selection, feature selection, data processing, model development for training and validation, and finally, results obtained from prediction tests. With this it was possible to obtain a percentage of 72,222 %, allowing the K-Net95 model to be cataloged as a stable and efficient network, despite the computational limitations to which the project was limited.

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Authors

  • Maria Camila Castaño

    Universidad Tecnológica de Pereira

  • Jonnatan Arias Garcia

    Colombia

  • Walter Serna Serna

    Colombia

  • Alvaro Angel Orozco Gutierrez

    Colombia

  • David Augusto Cardenas Peña

    Colombia

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