Detection of Alzheimer's using magnetic resonance imaging (MRI) images through Machine Learning
DOI:
https://doi.org/10.47185/27113760.v4n2.121Keywords:
Artificial Intelligence, Alzheimer, Machine Learning, Neural NetworksAbstract
Alzheimer is a neurodegenerative disease that affects millions of people worldwide. This article addresses the early detection of the disease through the analysis of magnetic resonance imaging (MRI) images and the use of machine learning techniques. A comprehensive literature review was conducted on previous research in this field, which identified the current challenges and limitations in Alzheimer's detection using MRI. In particular, existing methods were found to be costly, time-consuming, and not accurate enough. Consequently, three machine learning models (SVM, decision trees, and neural networks) were trained and tested for Alzheimer's detection using MRI images. Their ability to classify images as belonging to an Alzheimer's patient or a healthy one was evaluated. The results showed that all three models achieve high accuracy in Alzheimer's detection, with the neural network offering the best performance. Moreover, the selection of relevant features was found to be crucial for improving the models' performance. The use of machine learning techniques for the early detection of Alzheimer's through MRI images is a valuable tool. The proposed models in this article demonstrate a high degree of accuracy and speed, making them effective alternatives to conventional methods.
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