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Hippocampus-Based Alzheimer’s Severity Classification Using a Support Vector Machine Framework on Multi-Modal MRI Data
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Keywords
Alzheimer’s disease
hippocampus
support vector machine
MRI
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Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and hippocampal atrophy. Accurate classification of disease severity is crucial for timely intervention and personalized treatment strategies. This study presents a hippocampus-based Alzheimer’s severity classification framework leveraging support vector machine (SVM) methodology applied to multi-modal magnetic resonance imaging (MRI) data. The framework integrates advanced feature extraction, pre-processing, and SVM-based classification to differentiate mild, moderate, and severe Alzheimer stages. Using a comparative synthesis of existing studies, the model demonstrates enhanced diagnostic accuracy and robustness. The proposed approach addresses existing gaps in automated severity classification by focusing on hippocampal structural characteristics and multi-modal data integration, providing a reliable tool for clinical and research applications.
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