...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Multimedia Tools and Applications >Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in X-ray image datasets
【24h】

Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in X-ray image datasets

机译：使用X射线图像数据集中的Metaheuristic优化算法的双重特征选择和重新平衡策略

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 imbalance and multi-dimension are two common problems in the medical image datasets, which affect the performances of the image processing procedures. The traditional methods to solve these two problems are notoriously difficult. Accordingly, this work employed metaheuristic methods to optimize the rebalancing process of the imbalanced class distribution for further use in the feature selection procedure for dimensionality reduction for the medical X-ray image datasets. Different metaheuristic algorithms were used to maximize the parameter values of the rebalancing and feature selection phases to preprocess the datasets. The proposed work devised a multi-objective optimization strategy in the process of the metaheuristic algorithms search to solve the problem of dual imbalanced dataset and feature selection. Afterward, a comparative study of the proposed optimized approach with the conventional methods was conducted to evaluate the proposed method performance. The results established the superiority of the proposed method to overcome the imbalanced and multi-dimensional problem. The proposed method generated a reasonable number of minority class samples and selected a sensible subset of features to ultimately obtain a very extraordinary accuracy with great credibility from a negative value of kappa and a false high accuracy. It produced higher credibility and correctness classification performance in the practical problem of medical X-ray images compared to other algorithms. Feature selection with Random-SMOTE (RSMOTE) using the self-adaptive Bat algorithm is superior to the optimization using particle swarm optimization. The proposed method using the Bat algorithm achieved 94.6% classification accuracy with 0.883 Kappa value using the lung X-ray first dataset.
机译：不平衡和多维尺寸是医学图像数据集中的两个常见问题，这会影响图像处理过程的性能。解决这两个问题的传统方法是众所周知的。因此，该工作采用了所造胸部方法来优化不平衡类分布的重新平衡过程，以便在特征选择过程中进一步用于医疗X射线图像数据集的维数减少。使用不同的成帧算法来最大化重新平衡的参数值和特征选择阶段以预处理数据集。所提出的工作在成群质算法搜索过程中设计了一种多目标优化策略，以解决双重不平衡数据集和特征选择的问题。之后，进行了对常规方法的提出的优化方法的比较研究，评价了所提出的方法性能。结果建立了克服不平衡和多维问题的提出方法的优势。所提出的方法产生了合理数量的少数群体样本，并选择了一个明智的特征子集，最终获得了非常非常精确的准确性，从Kappa的负值和虚假的高精度。与其他算法相比，它在医学X射线图像的实际问题中产生了更高的可信度和正确性分类性能。使用自适应BAT算法随机扫描（RSMOTE）的特征选择优于使用粒子群优化优化。使用BAT算法的所提出的方法实现了使用肺X射线第一个数据集的0.883 kappa值的94.6％的分类精度。

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