Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Assisted Magnetic Resonance Imaging Diagnosis for Alzheimer's Disease Based on Kernel Principal Component Analysis and Supervised Classification Schemes

  • Wang, Yu (School of Artificial Intelligence, Beijing Technology and Business University) ;
  • Zhou, Wen (School of Artificial Intelligence, Beijing Technology and Business University) ;
  • Yu, Chongchong (School of Artificial Intelligence, Beijing Technology and Business University) ;
  • Su, Weijun (School of Artificial Intelligence, Beijing Technology and Business University)
  • Received : 2018.12.14
  • Accepted : 2019.10.14
  • Published : 2021.02.28
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Abstract

Alzheimer's disease (AD) is an insidious and degenerative neurological disease. It is a new topic for AD patients to use magnetic resonance imaging (MRI) and computer technology and is gradually explored at present. Preprocessing and correlation analysis on MRI data are firstly made in this paper. Then kernel principal component analysis (KPCA) is used to extract features of brain gray matter images. Finally supervised classification schemes such as AdaBoost algorithm and support vector machine algorithm are used to classify the above features. Experimental results by means of AD program Alzheimer's Disease Neuroimaging Initiative (ADNI) database which contains brain structural MRI (sMRI) of 116 AD patients, 116 patients with mild cognitive impairment, and 117 normal controls show that the proposed method can effectively assist the diagnosis and analysis of AD. Compared with principal component analysis (PCA) method, all classification results on KPCA are improved by 2%-6% among which the best result can reach 84%. It indicates that KPCA algorithm for feature extraction is more abundant and complete than PCA.

Keywords

Acknowledgement

This work is supported by Joint Project of Beijing Natural Science Foundation and Beijing Municipal Education Commission (No. KZ202110011015), and National Natural Science Foundation of China (No. 61671028).

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