期刊
NEUROCOMPUTING
卷 320, 期 -, 页码 195-202出版社
ELSEVIER
DOI: 10.1016/j.neucom.2018.09.001
关键词
Alzheimer's disease; Support vector machine; Switching delayed particle swarm optimization; Classification; Principal component analysis
资金
- UK-China Industry Academia Partnership Programme [UK-CIAPP-276]
- Korea Foundation for Advanced Studies
- Research Fund for the Taishan Scholar Project of Shandong Province of China
- Natural Science Foundation of China [61403319, 61773124, 61873148]
In healthcare sector, it is of crucial importance to accurately diagnose Alzheimer's disease (AD) and its prophase called mild cognitive impairment (MCI) so as to prevent degeneration and provide early treatment for AD patients. In this paper, a framework is proposed for the diagnosis of AD, which consists of MRI images preprocessing, feature extraction, principal component analysis, and the support vector machine (SVM) model. In particular, a new switching delayed particle swarm optimization (SDPSO) algorithm is proposed to optimize the SVM parameters. The developed framework based on the SDPSO-SVM model is successfully applied to the classification of AD and MCI using MRI scans from ADNI dataset. Our developed algorithm can achieve excellent classification accuracies for 6 typical cases. Furthermore, experiment results demonstrate that the proposed algorithm outperforms several SVM models and also two other state-of-art methods with deep learning embedded, thereby serving as an effective AD diagnosis method. (C) 2018 Elsevier B.V. All rights reserved.
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