A new approach for clustered MCs classification with sparse features learning and twsvm

The Scientific World Journal

Volumn 2014, Issue , 2014, Pages

  Zhang, Xin Sheng ^a  

^a XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BREAST CANCER; CANCER CLASSIFICATION; CANCER DIAGNOSIS; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC EQUIPMENT; DIGITAL MAMMOGRAPHY; DISCRIMINANT ANALYSIS; MICROCALCIFICATION CLUSTER BASED ON SPARSE REPRESENTATION; PERFORMANCE; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; TWIN SUPPORT VECTOR MACHINE; ALGORITHM; AUTOMATED PATTERN RECOGNITION; BREAST TUMOR; CALCINOSIS; COMPUTER ASSISTED DIAGNOSIS; FEMALE; HUMAN; MAMMOGRAPHY; METHODOLOGY; PATHOLOGY; RADIOGRAPHY;

ALGORITHMS; BREAST NEOPLASMS; CALCINOSIS; FEMALE; HUMANS; MAMMOGRAPHY; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; ROC CURVE; SUPPORT VECTOR MACHINES;

EID: 84896270629     PISSN: None     EISSN: 1537744X     Source Type: Journal    
DOI: 10.1155/2014/970287     Document Type: Article

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