摘要
为了更有效而准确地诊断阿尔茨海默病(Alzheimer’s disease,AD)和轻度认知障碍(Mild Cognitive Impairment,MCI),文章提出了一种基于多模态数据(MRI、PET和非成像数据CSF)的集成支持向量机来分类AD和MCI。该算法使用集成学习技术来综合利用不同模态数据之间相互作用产生的分类判别信息,并利用支持向量机进行分类。为了评价该算法的有效性,采用十折(10-fold)交叉验证策略来验证其性能,并在标准数据集ADNI上测试算法性能。实验结果表明,多模态集成支持向量机分类方法的性能优于多模态多核学习和单模态方法。
To effectively diagnose Alzheimer’s disease (AD) and mild cognitive impairment (MCI), a multimodal ensemble support vector machine (SVM) based on multi-modality data was proposed and used for the classiifcation of AD and MCI. The ensemble learning was employed and the discrimination information of classiifcation was extracted from different multiple modalities data, then the SVM was used for classiifcation of AD and MCI. In order to validate the efifcacy of proposed method, a 10-fold cross-validation was used and tested on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method is better than multi-modality linear multiple kernel learning and single-modality method.
出处
《集成技术》
2013年第6期27-30,共4页
Journal of Integration Technology
基金
重庆市教委科学技术研究项目(No.KJ121111和KJ131108)
关键词
阿尔茨海默病
轻微认知障碍
多模态集成学习
支持向量机
Alzheimer's disease
mild cognitive impairment
multimodal ensemble learning
support vector machine
