Latent feature mining of spatial and marginal characteristics for mammographic mass classification

Neurocomputing

Volumn 144, Issue , 2014, Pages 107-118

  Wang, Ying ^a   Li, Jie ^a   Gao, Xinbo ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Computer aided diagnosis; LDA; Mammographic mass classification; Polarized spatial pyramid; Statistical marginal characteristic

Indexed keywords

CLASSIFICATION (OF INFORMATION); MAMMOGRAPHY; STATISTICS; SUPPORT VECTOR MACHINES;

CLASSIFICATION ACCURACY; COMPUTER AIDED DIAGNOSIS(CAD); EFFECTIVENESS AND EFFICIENCIES; LATENT DIRICHLET ALLOCATIONS; MAMMOGRAPHIC MASS; SPATIAL CHARACTERISTICS; SPATIAL PYRAMIDS; STATISTICAL MARGINAL CHARACTERISTIC;

COMPUTER AIDED DIAGNOSIS;

ACCURACY; ARTICLE; CLASSIFICATION; CLASSIFICATION ALGORITHM; DATA MINING; LATENT DIRICHLET ALLOCATION MODEL; MACHINE LEARNING; MAMMOGRAPHIC MASS CLASSIFICATION; MAMMOGRAPHY; MAMMOGRAPHY SYSTEM; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PRIORITY JOURNAL; PROCESS DEVELOPMENT; SENSITIVITY AND SPECIFICITY; SPATIAL ANALYSIS; STRUCTURAL EQUATION MODELING; SUPPORT VECTOR MACHINE;

EID: 84906055286     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.11.050     Document Type: Article

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