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.

翻译：痴呆作为一种常见的神经退行性疾病，是阿尔茨海默病（AD）的主要表现。随着病情从轻度发展到重度，个体的独立日常生活能力受到显著损害，亟需及时准确的AD分类。机器学习或深度学习模型已成为实现这一目标的有效工具。本研究提出了一种结合RF、SVM和CNN算法的方法，通过引入分水岭分割从MRI图像中提取特征，用于痴呆四个阶段的分类。结果表明，采用分水岭特征时，SVM的准确率高达96.25%，优于其他分类方法。我们利用ADNI数据集评估了该方法的有效性，并观察到分水岭分割的引入有助于提升模型性能。