...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Image Processing, IET >Ensemble of deep convolutional neural networks based multi-modality images for Alzheimer's disease diagnosis
【24h】

Ensemble of deep convolutional neural networks based multi-modality images for Alzheimer's disease diagnosis

机译：基于深度卷积神经网络的多模态图像集成，用于阿尔茨海默氏病诊断

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Alzheimer's disease (AD) is one of the most common progressive neurodegenerative diseases. Structural magnetic resonance imaging (MRI) would provide abundant information on the anatomical structure of human organs. Fluorodeoxy-glucose positron emission tomography (PET) obtains the metabolic activity of the brain. Previous studies have demonstrated that multi-modality images could contribute to improve diagnosis of AD. However, these methods need to extract the handcrafted features that demand domain specific knowledge and image processing stage is time consuming. In order to tackle these problems, in this study, the authors propose a novel framework that ensembles three state-of-the-art deep convolutional neural networks (DCNNs) with multi-modality images for AD classification. In detail, they extract some slices from each subject of each modality, and every DCNN generates a probabilistic score for the input slices. Furthermore, a 'dropout' mechanism is introduced to discard low discrimination slices of the category probabilities. Then average reserved slices of each subject are acquired as a new feature. Finally, they train the Adaboost ensemble classifier based on single decision tree classifier with the MRI and PET probabilistic scores of each DCNN. Evaluations on Alzheimer's Disease Neuroimaging Initiative database show that the proposed algorithm has better performance compared to existing method, the algorithm proposed in this study significantly improved the classification accuracy.
机译：阿尔茨海默氏病（AD）是最常见的进行性神经退行性疾病之一。结构磁共振成像（MRI）将提供有关人体器官解剖结构的大量信息。氟脱氧葡萄糖正电子发射断层扫描（PET）获得大脑的代谢活性。先前的研究表明，多模态图像可有助于改善AD的诊断。但是，这些方法需要提取需要领域特定知识的手工特征，并且图像处理阶段非常耗时。为了解决这些问题，在这项研究中，作者提出了一个新颖的框架，该框架将三个最新的深度卷积神经网络（DCNN）与多模式图像进行AD分类。详细地，他们从每种模态的每个主题中提取一些切片，并且每个DCNN都会为输入切片生成概率分数。此外，引入了一种“丢弃”机制以丢弃类别概率的低辨别力切片。然后，获取每个主题的平均保留片作为新特征。最后，他们使用单决策树分类器和每个DCNN的MRI和PET概率评分来训练Adaboost集成分类器。对阿尔茨海默氏病神经影像主动性数据库的评估表明，与现有方法相比，该算法具有更好的性能，该算法明显提高了分类精度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

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