...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Multimedia Tools and Applications >An adaptive fuzzy K-nearest neighbor approach for MR brain tumor image classification using parameter free bat optimization algorithm
【24h】

An adaptive fuzzy K-nearest neighbor approach for MR brain tumor image classification using parameter free bat optimization algorithm

机译：基于无参​​数蝙蝠优化算法的MR脑肿瘤图像自适应模糊K近邻算法

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

This paper presents an automatic diagnosis system for the tumor grade classification through magnetic resonance imaging (MRI). The diagnosis system involves a region of interest (ROI) delineation using intensity and edge magnitude based multilevel thresholding algorithm. Then the intensity and the texture attributes are extracted from the segregated ROI. Subsequently, a combined approach known as Fisher+ Parameter-Free BAT (PFreeBAT) optimization is employed to derive the optimal feature subset. Finally, a novel learning approach dubbed as PFree BAT enhanced fuzzy K-nearest neighbor (FKNN) is proposed by combining FKNN with PFree BAT for the classification of MR images into two categories: High and Low-Grade. In PFree BAT enhanced FKNN, the model parameters, i.e., neighborhood size k and the fuzzy strength parameter m are adaptively specified by the PFree BAT optimization approach. Integrating PFree BAT with FKNN enhances the classification capability of the FKNN. The diagnostic system is rigorously evaluated on four MR images datasets including images from BRATS 2012 database and the Harvard repository using classification performance metrics. The empirical results illustrate that the diagnostic system reached to ceiling level of accuracy on the test MR image dataset via 5-fold cross-validation mechanism. Additionally, the proposed PFree BAT enhanced FKNN is evaluated on the Parkinson dataset (PD) from the UCI repository having the pre-extracted feature space. The proposed PFree BAT enhanced FKNN reached to an average accuracy of 98% and 97.45%. with and without feature selection on PD dataset. Moreover, solely to contrast, the performance of the proposed PFree BAT enhanced FKNN with the existing FKNN variants the experimentations were also done on six other standard datasets from KEEL repository. The results indicate that the proposed learning strategy achieves the best value of accuracy in contrast to the existing FKNN variants.
机译：本文提出了一种通过磁共振成像（MRI）对肿瘤等级进行分类的自动诊断系统。该诊断系统涉及使用基于强度和边缘幅度的多级阈值算法对感兴趣区域（ROI）进行描述。然后从分离的ROI中提取强度和纹理属性。随后，采用称为Fisher +无参数BAT（PFreeBAT）优化的组合方法来得出最佳特征子集。最后，通过将FKNN与PFree BAT相结合，提出了一种被称为PFree BAT增强模糊K最近邻（FKNN）的新颖学习方法，用于将MR图像分为两类：高等级和低等级。在PFree BAT增强的FKNN中，模型参数（即邻域大小k和模糊强度参数m）由PFree BAT优化方法自适应地指定。将PFree BAT与FKNN集成可以增强FKNN的分类能力。使用分类性能指标，对四个MR图像数据集（包括来自BRATS 2012数据库和哈佛资源库的图像）进行了严格的评估，对诊断系统进行了评估。实验结果表明，该诊断系统通过5倍交叉验证机制在测试MR图像数据集上达到了准确性的最高水平。另外，在具有预提取特征空间的UCI存储库中的帕金森数据集（PD）上评估了拟议的PFree BAT增强FKNN。提出的PFree BAT增强FKNN的平均准确率达到98％和97.45％。在PD数据集上选择和不选择特征。此外，仅作对比，所提出的PFree BAT增强的FKNN与现有的FKNN变体的性能，还对KEEL存储库中的其他六个标准数据集进行了实验。结果表明，与现有的FKNN变体相比，所提出的学习策略可实现最佳的精度值。

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