Identifying Alzheimer Disease Dementia Levels Using Machine Learning Methods
This work addresses timely and accurate classification of Alzheimer's disease progression for medical diagnosis, but it is incremental as it combines existing methods.
The study tackled the problem of classifying Alzheimer's disease dementia stages from mild to severe using machine learning on MRI images, achieving an accuracy of 96.25% with SVM and watershed segmentation.
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.