首页> 外文OA文献 >Distinguishing Parkinson's disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks
【2h】

Distinguishing Parkinson's disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks

机译:使用PET数据和基于支持向量机和贝叶斯网络的计算机系统,将帕金森氏病与非典型帕金森氏症区分开

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

Differentiating between Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) is still a challenge, specially at early stages when the patients show similar symptoms. During last years, several computer systems have been proposed in order to improve the diagnosis of PD, but their accuracy is still limited. In this work we demonstrate a full automatic computer system to assist the diagnosis of PD using 18F-DMFP PET data. First, a few regions of interest are selected by means of a two-sample t-test. The accuracy of the selected regions to separate PD from APS patients is then computed using a support vector machine classifier. The accuracy values are finally used to train a Bayesian network that can be used to predict the class of new unseen data. This methodology was evaluated using a database with 87 neuroimages, achieving accuracy rates over 78%. A fair comparison with other similar approaches is also provided.
机译:区分帕金森氏病(PD)和非典型帕金森综合症(APS)仍然是一个挑战,特别是在患者表现出相似症状的早期。在过去的几年中,已经提出了几种计算机系统以改善PD的诊断,但是其准确性仍然受到限制。在这项工作中,我们演示了一种全自动计算机系统,该系统可以使用18F-DMFP PET数据协助诊断PD。首先,通过两次样本t检验选择一些感兴趣的区域。然后使用支持向量机分类器来计算将PD与APS患者分开的选定区域的准确性。最后,将精度值用于训练贝叶斯网络,该贝叶斯网络可用于预测新的看不见的数据的类别。使用具有87个神经图像的数据库对该方法进行了评估,其准确率超过78%。还提供了与其他类似方法的合理比较。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号