SVM-based glioma grading: Optimization by feature reduction analysis

Zeitschrift fur Medizinische Physik

Volumn 22, Issue 3, 2012, Pages 205-214

  Zöllner, Frank G ^a   Emblem, Kyrre E ^b,c   Schad, Lothar R ^a  

^a UNIVERSITY OF HEIDELBERG   (Germany)

^b MASSACHUSETTS GENERAL HOSPITAL   (United States)

^c OSLO UNIVERSITY HOSPITAL   (Norway)

Author keywords

Computer aided diagnostics (CAD); Feature reduction; Glioma grading; Machine learning; Perfusion MRI; Support vector machines

Indexed keywords

ADOLESCENT; ADULT; AGE DISTRIBUTION; AGED; ARTICLE; BLOOD VOLUME; CANCER GRADING; CHILD; CORRELATION COEFFICIENT; DIAGNOSTIC ACCURACY; FEMALE; GLIOMA; HISTOGRAM; HISTOPATHOLOGY; HUMAN; INDEPENDENT COMPONENT ANALYSIS; MAJOR CLINICAL STUDY; MALE; MATHEMATICAL COMPUTING; NUCLEAR MAGNETIC RESONANCE IMAGING; PREDICTIVE VALUE; PRINCIPAL COMPONENT ANALYSIS; PROCESS OPTIMIZATION; SCHOOL CHILD; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; TUMOR CLASSIFICATION;

ADOLESCENT; ADULT; AGED; AGED, 80 AND OVER; ALGORITHMS; BRAIN NEOPLASMS; CHILD; FEMALE; GLIOMA; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; NEOPLASM GRADING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINES; YOUNG ADULT;

EID: 84865593031     PISSN: 09393889     EISSN: 18764436     Source Type: Journal    
DOI: 10.1016/j.zemedi.2012.03.007     Document Type: Article

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