Application of KPCA and AdaBoost algorithm in classification of functional magnetic resonance imaging of Alzheimer’s disease

2020 ◽  
Vol 32 (10) ◽  
pp. 5329-5338 ◽  
Author(s):  
Zhao Fan ◽  
Fanyu Xu ◽  
Cai Li ◽  
Lili Yao
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Adaboost Algorithm

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

2021 ◽  
Author(s):  
Yu Wang ◽  
Hongfei Jia ◽  
Yifan Duan ◽  
Hongbing Xiao
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Brain Regions ◽  
Support Vector ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Public Data

Abstract 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.

scholarly journals Tracking Cognitive Change over 24 Weeks with Longitudinal Functional Magnetic Resonance Imaging in Alzheimer’s Disease

2012 ◽  
Vol 9 (4) ◽  
pp. 176-186 ◽  
Author(s):  
Donald G. McLaren ◽  
Aishwarya Sreenivasan ◽  
Eli L. Diamond ◽  
Meghan B. Mitchell ◽  
Koene R.A. Van Dijk ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Cognitive Change ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging
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