Improved Alzheimer's Disease Classification Using Innovative Multimodal Feature Selection and Fusion Technique
Keywords:
Alzheimer Disease (AD), Mild Cognitive Impairment (MCI), Normal Controls (NC), Feature SelectionAbstract
This paper aims to enhance the accuracy of Alzheimer's Disease (AD) versus Mild Cognitive Impairment (MCI) versus Normal Controls (NC) classification through the implementation of feature reduction techniques and the fusion of multimodal features. Specifically, gray matter within specified regions of interest (ROI) in the brain is extracted from both MRI and FDG-PET images. The LASSO feature selection technique is employed to identify relevant features crucial for distinguishing AD from MCI and NC. The reduction in features results in a 92.27% accuracy, reflecting an 11% improvement compared to classification without feature selection in AD versus MCI. The classification of AD versus MCI and MCI versus NC proves challenging due to the high correlations among features. The analysis reveals that a maximum classification accuracy of 92.27% is achieved for AD versus MCI through the multi-modal combination of features using a linear Support Vector Machine (SVM) algorithm. Additionally, a 99% accuracy is attained for MCI versus NC using the linear SVM algorithm. The fusion of features across all modalities yields a 94.9% accuracy for AD versus MCI versus NC. This analysis underscores that the fusion of multimodal features consistently improves classification accuracy compared to relying on any single modality. The study utilizes the ADNI-1 database, and the corresponding subject IDs are detailed in Table 10.
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