Applying 3DPCANet and Functional Magnetic Resonance Imaging to Aided Diagnosis of Alzheimer’s Disease

Alzheimer's disease (AD) is a progressive neurodegenerative disease, which changes the structure of brain regions by some hidden causes. In this paper for assisting doctors to make correct judgments, an improved 3DPCANet method is proposed to classify AD by combining the mean (mALFF) of the whole brain. The main idea includes that firstly, the functional magnetic resonance imaging (fMRI) data is pre-processed, and mALFF is calculated to get the corresponding matrix. Then the features of mALFF images are extracted via the improved 3DPCANet network. Finally, AD patients with different stages are classified using support vector machine (SVM). Experiments results based on public data from the Alzheimer’s disease neuroimaging initiative (ADNI) show that the proposed approach has better performance compared with state-of-the-art methods. The accuracies of AD vs. significant memory concern (SMC), SMC vs. late mild cognitive impairment (LMCI), and normal control (NC) vs. SMC reach respectively 92.42%, 91.80%, and 89.50%, which testifies the feasibility and effectiveness of the proposed method.