Breast cancer discriminant feature analysis for diagnosis via jointly sparse learning

Neurocomputing

Volumn 177, Issue , 2016, Pages 198-205

  Kong, Heng ^a,c   Lai, Zhihui ^b,c   Wang, Xu ^b   Liu, Feng ^b  

^a Shen Zhen Nan Shan District People's Hospital   (China)

^b SHENZHEN UNIVERSITY   (China)

^c MEDICAL COLLEGE OF GEORGIA   (United States)

Author keywords

Breast cancer; Diagnosis and classification; Feature analysis; Sparse learning

Indexed keywords

ALGORITHMS; COMPUTER AIDED DIAGNOSIS; DIAGNOSIS; DISCRIMINANT ANALYSIS; FACTOR ANALYSIS; FEATURE EXTRACTION; FORECASTING; ITERATIVE METHODS; LEARNING ALGORITHMS; OPTIMIZATION;

BREAST CANCER; DISCRIMINANT VECTORS; FEATURE ANALYSIS; OPTIMIZATION PROBLEMS; PATHOLOGIC DIAGNOSIS; PRACTICAL EXPERIENCE; PREDICTION ACCURACY; SPARSE LEARNING;

DISEASES;

ARTICLE; BREAST CANCER; CANCER DIAGNOSIS; CANCER RESEARCH; DATA ANALYSIS; DATA BASE; DIAGNOSTIC ACCURACY; DISCRIMINANT ANALYSIS; EXPERIMENTAL STUDY; FACTORIAL ANALYSIS; INTERMETHOD COMPARISON; JOINTLY SPARSE DISCRIMINANT ANALYSIS; LEARNING ALGORITHM; PRACTICE GUIDELINE; PREDICTION; PRIORITY JOURNAL; PROCESS OPTIMIZATION; SAMPLE SIZE;

EID: 84959468244     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.11.033     Document Type: Article

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