Computer-aided diagnosis has become a widely-used auxiliary tool for the diagnosis of Alzheimer's disease (AD). In this study, we developed an extreme learning machine (ELM) model to discriminate between patients with AD and normal controls (NCs) using voxel-based morphometry (VBM) obtained from magnetic resonance imaging. Support vector machine (SVM), Gaussian process regression (GPR), and partial least squares (PLS) regression were compared with the ELM model. The calculated characteristics, i.e., texture features, VBM parameters, and clinical information, were adopted as the classification features. A 10-fold cross validation was used to evaluate the performance of ELM, SVM, GPR, and PLS models. We applied the proposed methods to data from 58 patients with AD and 94 NCs, and achieved a classification accuracy of up to 0.96 with all classification features of the ELM model, while the results of the other three models were 0.82 (PLS), 0.79 (GPR), and 0.75 (SVM). Furthermore, the effect of VBM parameter modeling is better than texture parameter. Thus, our method was optimal in distinguishing patients with AD from NCs, and may therefore be useful for the diagnosis of AD.

译文

计算机辅助诊断已成为诊断阿尔茨海默氏病 (AD) 的广泛使用的辅助工具。在这项研究中,我们开发了一种极端学习机 (ELM) 模型,以使用从磁共振成像获得的基于体素的形态计量学 (VBM) 来区分AD患者和正常对照 (NCs)。将支持向量机 (SVM),高斯过程回归 (GPR) 和偏最小二乘 (PLS) 回归与ELM模型进行了比较。将计算出的特征 (即纹理特征,VBM参数和临床信息) 用作分类特征。使用10倍交叉验证来评估ELM,SVM,GPR和PLS模型的性能。我们将所提出的方法应用于58例AD患者和94例NCs的数据,在ELM模型的所有分类特征下,分类准确率高达0.96,而其他三种模型的结果分别是0.82 (PLS) 、0.79 (GPR) 和0.75 (SVM)。此外,VBM参数建模的效果优于纹理参数。因此,我们的方法在区分AD患者和NCs方面是最佳的,因此可能对AD的诊断有用。

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