Abstract
To address the challenging task of diagnosing neurodegenerative brain disease, such as Alzheimer's disease (AD) and mild cognitive impairment (MCI), we propose a novel method using discriminative feature learning and canonical correlation analysis (CCA) in this paper. Specifically, multimodal features and their CCA projections are concatenated together to represent each subject, and hence both individual and shared information of AD disease are captured. A discriminative learning with multilayer feature hierarchy is designed to further improve performance. Also, hybrid representation is proposed to maximally explore data from multiple modalities. A novel normalization method is devised to tackle the intra- and inter-subject variations from the multimodal data. Based on our extensive experiments, our method achieves an accuracy of 96.93% [AD vs. normal control (NC)], 86.57 % (MCI vs. NC), and 82.75% [MCI converter (MCI-C) vs. MCI non-converter (MCI-NC)], respectively, which outperforms the state-of-the-art methods in the literature.
Highlights
Brain disease or disorder [i.e., Alzheimer’s disease (AD), Parkinson’s disease] has arisen as a serious social issue in line with aging populations and has garnered great attention over the past decade
Based on our extensive experiments on the ADNI dataset, our method achieves a classification accuracy of 96.93% (AD vs. normal control (NC)), 86.75% (MCI vs. NC), and 82.75% [mild cognitive impairment (MCI) converter (MCI-C) vs. MCI non-converter (MCI-NC)], which outperforms the state-of-the-art methods in the literature
The publicly available ADNI dataset initialized by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies, and non-profit organizations has been utilized for performance evaluation
Summary
Brain disease or disorder [i.e., Alzheimer’s disease (AD), Parkinson’s disease] has arisen as a serious social issue in line with aging populations and has garnered great attention over the past decade. The Alzheimer’s Association (2014) has reported that the number of elderly people with either AD or MCI increases significantly, and it is of great importance for early diagnosis and symptomatic treatments of the disease. There are many methods to diagnose this neurological disease via feature or score fusion (Perrin et al, 2009; Wee et al, 2011; Catana et al, 2012; Westman et al, 2012; Ramirez et al, 2013; Jiang and Lai, 2014; Suk et al, 2014; Zhu et al, 2014a; Lei et al, 2015a)
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