Texture feature ranking with relevance learning to classify interstitial lung disease patterns
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摘要
ObjectiveThe generalized matrix learning vector quantization (GMLVQ) is used to estimate the relevance of texture features in their ability to classify interstitial lung disease patterns in high-resolution computed tomography images.
论文关键词：Feature selection,Relevance learning,Supervised learning,Texture analysis,High-resolution computed tomography of the chest,Interstitial lung disease patterns
论文评审过程：Received 26 August 2011, Revised 26 May 2012, Accepted 12 July 2012, Available online 23 September 2012.
论文官网地址：https://doi.org/10.1016/j.artmed.2012.07.001
