Cancer diagnosis by nuclear morphometry using spatial information

Pattern Recognition Letters

Volumn 42, Issue 1, 2014, Pages 115-121

  Huang, Hu ^a   Tosun, Akif Burak ^a   Guo, Jia ^a   Chen, Cheng ^a   Wang, Wei ^a   Ozolek, John A ^b   Rohde, Gustavo K ^a,c  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b CHILDREN S HOSPITAL OF PITTSBURGH   (United States)

^c Department of Electrical and Computer Engineering Carnegie Mellon University ^*  (United States)

Author keywords

Cancer diagnosis; Majority voting; Na ve Bayes; Set classification; Thyroid lesion classification

Indexed keywords

PATTERN RECOGNITION; SOFTWARE ENGINEERING;

CANCER DIAGNOSIS; CLASSIFICATION ACCURACY; LESION CLASSIFICATION; MAJORITY VOTING; NUCLEAR MEASUREMENT; QUANTITATIVE INFORMATION; SPATIAL INFORMATIONS; STATISTICAL DEPENDENCIES;

DISEASES;

EID: 84896534754     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2014.02.008     Document Type: Article

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