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Multimodal Imaging For Enhanced Diagnosis And For Assessing Progression Of Alzheimer’S Disease, Chunfei Li
Multimodal Imaging For Enhanced Diagnosis And For Assessing Progression Of Alzheimer’S Disease, Chunfei Li
FIU Electronic Theses and Dissertations
A neuroimaging feature extraction model is designed to extract region-based image features whose values are predicted by base learners trained on raw neuroimaging morphological variables. The main objectives are to identify Alzheimer’s disease (AD) in its earliest manifestations, and be able to predict and gauge progression of the disease through the stages of mild cognitive impairment (EMCI), late MCI (LMCI) and AD. The model was evaluated on the ADNI database and showed 75.26% accuracy for the challenging EMCI diagnosis based on the 10-fold cross-validation. Our approach also performed well for the other binary classifications: EMCI vs. LMCI (72.3%), EMCI vs. …