Identifying Alzheimer Disease Dementia Levels Using Machine Learning Methods

TL;DR

This paper presents a machine learning approach combining watershed segmentation with classifiers like SVM, RF, and CNN to accurately classify four stages of Alzheimer’s disease dementia from MRI images, achieving up to 96.25% accuracy.

Contribution

The study introduces the integration of watershed segmentation with machine learning models for improved Alzheimer’s dementia staging from MRI data.

Findings

SVM with watershed features achieved 96.25% accuracy

Watershed segmentation enhances feature extraction and model performance

The method outperforms other classification approaches on ADNI dataset

Abstract

Dementia, a prevalent neurodegenerative condition, is a major manifestation of Alzheimer's disease (AD). As the condition progresses from mild to severe, it significantly impairs the individual's ability to perform daily tasks independently, necessitating the need for timely and accurate AD classification. Machine learning or deep learning models have emerged as effective tools for this purpose. In this study, we suggested an approach for classifying the four stages of dementia using RF, SVM, and CNN algorithms, augmented with watershed segmentation for feature extraction from MRI images. Our results reveal that SVM with watershed features achieves an impressive accuracy of 96.25%, surpassing other classification methods. The ADNI dataset is utilized to evaluate the effectiveness of our method, and we observed that the inclusion of watershed segmentation contributes to the enhanced performance of the models.