基于极限学习机的阿尔兹海默病辅助诊断

doi:10.3969/j.issn.0258-8021.2020.03.05

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Abstract Alzheimer's disease is a progressive disease of dementia usually associated with brain atrophy. We proposed a method of diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) with the anatomical features of MRI brain images. Method: The data were obtained from ADNI dataset, and the anatomical features of 818 subjects were computed by FreeSurfer software, these features were first preprocessed with age correction algorithm using linear regression to estimate normal aging effect, and was then removed from features. The extreme learning machine was utilized as classifier for diagnosis of AD and MCI with these preprocessed features. The ten-fold cross validation was adopted for calculating accuracy, sensitivity, specificity and area under curve (AUC). Results: By making average with 100 runs, the accuracy of diagnosis of AD was 87.62%, and the AUC reached 94.25%. The accuracy of diagnosis of MCI was 73.38%, and the sensitivity reached 83.88%. The age correction can improve the accuracy of MCI diagnosis. The results demonstrated the efficacy of the proposed method for diagnosis of AD and MCI.

Key words： Alzheimer's disease (AD) extreme learning machine (ELM) mild cognitive impairment (MRI) age correction computer-aided diagnosis (CAD)

Received: 13 August 2018

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R318

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	Articles by authors
	Lin Weiming
	Yuan Jiangnan
	Feng Chenwei
	Du Min

Cite this article:

Lin Weiming,Yuan Jiangnan,Feng Chenwei, et al. Computer-Aided Diagnosis of Alzheimer's Disease Based on Extreme Learning Machine[J]. Chinese Journal of Biomedical Engineering, 2020, 39(3): 288-294.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2020.03.05 OR http://cjbme.csbme.org/EN/Y2020/V39/I3/288

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