...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of medical systems >A hybrid fuzzy-neural system for computer-aided diagnosis of ultrasound kidney images using prominent features.
【24h】

A hybrid fuzzy-neural system for computer-aided diagnosis of ultrasound kidney images using prominent features.

机译：混合模糊神经系统，用于利用突出特征对超声肾脏图像进行计算机辅助诊断。

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

The objective of this work is to develop and implement a computer-aided decision support system for an automated diagnosis and classification of ultrasound kidney images. The proposed method distinguishes three kidney categories namely normal, medical renal diseases and cortical cyst. For the each pre-processed ultrasound kidney image, 36 features are extracted. Two types of decision support systems, optimized multi-layer back propagation network and hybrid fuzzy-neural system have been developed with these features for classifying the kidney categories. The performance of the hybrid fuzzy-neural system is compared with the optimized multi-layer back propagation network in terms of classification efficiency, training and testing time. The results obtained show that fuzzy-neural system provides higher classification efficiency with minimum training and testing time. It has also been found that instead of using all 36 features, ranking the features enhance classification efficiency. The outputs of the decision support systems are validated with medical expert to measure the actual efficiency. The overall discriminating capability of the systems is accessed with performance evaluation measure, f-score. It has been observed that the performance of fuzzy-neural system is superior compared to optimized multi-layer back propagation network. Such hybrid fuzzy-neural system with feature extraction algorithms and pre-processing scheme helps in developing computer-aided diagnosis system for ultrasound kidney images and can be used as a secondary observer in clinical decision making.
机译：这项工作的目的是开发和实施用于自动诊断和分类超声肾脏图像的计算机辅助决策支持系统。所提出的方法将正常肾脏，医学肾脏疾病和皮质囊肿分为三个肾脏类别。对于每个预处理的超声肾脏图像，提取了36个特征。已经开发了两种类型的决策支持系统，即优化的多层反向传播网络和混合模糊神经系统，这些功能具有对肾脏类别进行分类的功能。在分类效率，训练和测试时间方面，将混合模糊神经系统的性能与优化的多层反向传播网络进行了比较。结果表明，模糊神经系统以最小的训练和测试时间提供了更高的分类效率。还发现代替对全部36个特征进行使用，对特征进行排名可以提高分类效率。决策支持系统的输出经过医学专家的验证，以衡量实际效率。系统的总体区分能力可通过性能评估指标f分数来访问。已经观察到，与优化的多层反向传播网络相比，模糊神经系统的性能优越。这种具有特征提取算法和预处理方案的混合模糊神经系统，有助于开发超声肾脏图像的计算机辅助诊断系统，并可用作临床决策的辅助观察者。

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