JIPS (한국정보처리학회)
Current Result Document : 65 / 66
| 한글제목(Korean Title) |
Assisted Magnetic Resonance Imaging Diagnosis for Alzheimer’s Disease Based on Kernel Principal Component Analysis and Supervised Classification Schemes |
| 영문제목(English Title) |
Assisted Magnetic Resonance Imaging Diagnosis for Alzheimer’s Disease Based on Kernel Principal Component Analysis and Supervised Classification Schemes |
| 저자(Author) |
Chongchong Yu
Weijun Su
Yu Wang
Wen Zhou
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| 원문수록처(Citation) |
VOL 17 NO. 01 PP. 0178 ~ 0190 (2021. 02) |
한글내용
(Korean Abstract) |
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영문내용
(English 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.
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| 키워드(Keyword) |
Alzheimer’s Disease
Feature Extraction
KPCA
Machine Learning
Structural Magnetic Resonance Imaging
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| 파일첨부 |
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