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| Diagnosis of Alzheimer′s Disease via Multi-Modal Canonical Feature Representation |
| Zhuo Yinan ,Yang Peng ,Deng Yun, Ni Dong ,Lei Baiying*,Wang Tianfu* |
| Department of Biomedical Engineering, School of Medicine, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, Guangdong, China |
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Abstract To better explore the underlying disease patterns, a study of modeling the disease procession of Alzheimer′s disease (AD) for early diagnosis of mild cognitive impairment(MCI)is necessary. In this work, we proposed a new method to represent the multi-modal correlations of ROI features for AD diagnosis. First, we applied canonical correlation analysis (CCA) to discover the relationships of ROIs among different modalities and specifically with sparse least square regression loss function to acquire the discriminative features. Then we trained a classification model via support vector machine (SVM) by using the selected features for diagnosis. The empirical studies with 805 subjects downloaded from the Alzheimer′s Disease Neuroimaging Initiative (ADNI) database showed that our method achieved promising improvement when compared with others methods. We achieved 92.01% in AD vs NC (normal control), 74.83% in MCI vs NC and 70.27% in p-MCI (progressive-mild cognitive impairment) vs s-MCI (stable-mild cognitive impairment). In conclusion, the proposed method is beneficial to the early diagnosis of the disease.
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Received: 27 March 2017
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