  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.
