Feature Selection Based on Gaussian Mixture Model Clustering for the Classification of Pulmonary Nodules Based on Computed Tomography
作者: Duan HuihongWang XuHe XingyiHe YonggangSong LitaoNie Shengdong
刊名: Journal of Medical Imaging and Health Informatics, 2020, Vol.10 (5), pp.1033-1039
来源数据库: American Scientific Publishers
DOI: 10.1166/jmihi.2020.3008
关键词: Computed TomographyFeature SelectionGaussian Mixture ModelPulmonary Nodules
原始语种摘要: : In the pulmonary nodules computer aided diagnosis systems (CAD, feature selection plays an important role in reducing the false positive rate and improving the system accuracy. To solve the problem of feature selection techniques by which the diversity of featureswas damaged in the process of distinguishing malignant pulmonary nodules from benign pulmonary nodules, this study developed a novel feature selection algorithm for improving the accuracy of traditional computer-aided differential diagnosis for benign and malignant classification of pulmonarynodules.;: Firstly, we divided the extracted features of nodules into several groups by using Gaussian mixture model (GMM. Secondly, we applied Relief and sequential forward selection (SFS algorithm to find local optimum features dataset...
全文获取路径: 美国科学出版社 

  • Selection 分选
  • features 特征
  • aided 半自动
  • computer 电子计算机
  • improving 改进
  • accuracy 准确度
  • nodules 瘤状体
  • algorithm 算法
  • classifier 分级机
  • CAD Character Assemble/Disassemble