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首页> 外文会议>2013 Fourth IEEE International Workshop on Computational Intelligence in Medical Imaging >Detection of onset of Alzheimer's disease from MRI images using a GA-ELM-PSO classifier
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Detection of onset of Alzheimer's disease from MRI images using a GA-ELM-PSO classifier

机译：使用GA-ELM-PSO分类器从MRI图像中检测阿尔茨海默氏病的发作

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In this paper, a novel method for detecting the onset of Alzheimer's disease (AD) from Magnetic Resonance Imaging (MRI) scans is presented. It uses a combination of three different machine learning algorithms in order to get improved results and is based on a three-class classification problem. The three classes for classification considered in this study are normal, very mild AD and mild and moderate AD subjects. The machine learning algorithms used are: the Extreme Learning Machine (ELM) for classification, with its performance optimized by a Particle Swarm Optimization (PSO) and a Genetic algorithm (GA) used for feature selection. A Voxel-Based Morphometry (VBM) approach is used for feature extraction from the MRI images and GA is used to reduce the high dimensional features needed for classification. The GA-ELM-PSO classifier yields an average training accuracy of 94.57 % and a testing accuracy of 87.23 %, averaged across the three classes, over ten random trials. The results clearly indicate that the proposed approach can differentiate between very mild AD and normal cases more accurately, indicating its usefulness in detecting the onset of AD.
机译：本文提出了一种从磁共振成像（MRI）扫描中检测阿尔茨海默氏病（AD）发病的新方法。它使用三种不同的机器学习算法的组合以获得更好的结果，并且基于三类分类问题。本研究中考虑的三类分类是正常，非常轻度的AD和轻度和中度的AD对象。所使用的机器学习算法为：用于分类的极限学习机（ELM），其性能通过粒子群优化（PSO）和用于选择特征的遗传算法（GA）进行了优化。基于体素的形态计量学（VBM）方法用于从MRI图像中提取特征，而GA用于减少分类所需的高维特征。 GA-ELM-PSO分类器在十项随机试验中，三个类别的平均训练准确性为94.57％，测试准确性为87.23％。结果清楚地表明，所提出的方法可以更准确地区分轻度AD和正常病例，表明其在检测AD发作中的有用性。

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