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首页> 外文会议>International conference on neural information processing;ICONIP 2009 >Combination of Multiple Features in Support Vector Machine with Principal Component Analysis in Application for Alzheimer's Disease Diagnosis
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Combination of Multiple Features in Support Vector Machine with Principal Component Analysis in Application for Alzheimer's Disease Diagnosis

机译：支持向量机的多种特征与主成分分析相结合在阿尔茨海默病诊断中的应用

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Alzheimer's disease (AD) is a progressively neuro-degenerative disorder characterized by symptoms such as memory loss and cognitive degeneration. In the AD-related research, the volumetric analysis of hippocampus is the most extensive study. However, the segmentation and identification of the hippocampus are highly complicated and time-consuming. Therefore, we designed a MRI-based classification framework to distinguish AD's patients from normal individuals. First, volumetric features and shape features were extracted from MRI data. Afterward, Principle component analysis (PCA) was utilized to decrease the dimensions of feature space. Finally, a SVM classifier was trained for AD classification. With the proposed framework, the classification accuracy is improved from 73.08% or 76.92%, by only using volumetric features or shape features, to 92.31% by using three kinds of volume features and two kinds of shape features.
机译：阿尔茨海默氏病（AD）是一种进行性神经退行性疾病，其特征是记忆力减退和认知退化等症状。在与AD有关的研究中，海马体的体积分析是最广泛的研究。但是，海马的分割和识别非常复杂且耗时。因此，我们设计了一个基于MRI的分类框架，以区分AD患者与正常患者。首先，从MRI数据中提取体积特征和形状特征。之后，利用主成分分析（PCA）来减小特征空间的尺寸。最后，对SVM分类器进行了AD分类的训练。在提出的框架下，仅使用体积特征或形状特征，分类准确率从73.08％或76.92％提高到使用三种体积特征和两种形状特征的分类准确率达到92.31％。

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