干旱区生态环境知识资源中心(Arid): An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

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DOI	10.1002/ima.22304
	An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification
	Neffati, Syrine 1; Ben Abdellafou, Khaoula 2; Jaffel, Ines 1; Taouali, Okba 1; Bouzrara, Kais 1
通讯作者	Taouali, Okba
来源期刊	INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
ISSN	0899-9457
EISSN	1098-1098
出版年	2019
卷号	29 期号:2 页码:121-131
英文摘要	Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.
英文关键词	Alzheimer's disease downsized kernel principal component analysis medical image diagnosis mksvm multiobjective optimization
类型	Article
语种	英语
国家	Tunisia
收录类别	SCI-E
WOS记录号	WOS:000467272300003
WOS类目	Engineering, Electrical & Electronic ; Optics ; Imaging Science & Photographic Technology
WOS研究方向	Engineering ; Optics ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/216461
作者单位	1.Univ Monastir, Natl Engn Sch Monastir, Dept Elect, Monastir, Tunisia; 2.Univ Sousse, Higher Inst Comp Sci & Commun Technol ISITCom, MARS Modeling Automated Reasoning Syst Res Lab LR, Sousse, Tunisia
推荐引用方式 GB/T 7714	Neffati, Syrine,Ben Abdellafou, Khaoula,Jaffel, Ines,et al. An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification[J],2019,29(2):121-131.
APA	Neffati, Syrine,Ben Abdellafou, Khaoula,Jaffel, Ines,Taouali, Okba,&Bouzrara, Kais.(2019).An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification.INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY,29(2),121-131.
MLA	Neffati, Syrine,et al."An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification".INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 29.2(2019):121-131.