Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry

Investigative Ophthalmology and Visual Science

Volumn 43, Issue 1, 2002, Pages 162-169

  Goldbaum, M H ^a   Sample, P A ^a   Chan, K ^a   Williams, J ^a   Lee, T W ^a   Blumenthal, E ^a   Girkin, C A ^a   Zangwill, L M ^a   Bowd, C ^a   Sejnowski, T ^a   Weinreb, R N ^a  

^a Department of Ophthalmology ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGED; AREA UNDER THE CURVE; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; COMPUTER ASSISTED PERIMETRY; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; GLAUCOMA; HUMAN; INTERMETHOD COMPARISON; MAJOR CLINICAL STUDY; MEDICAL EXPERT; OPTIC NERVE; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; VISUAL FIELD;

DIAGNOSIS, COMPUTER-ASSISTED; FALSE NEGATIVE REACTIONS; GLAUCOMA; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); OPTIC NERVE; PERIMETRY; PHOTOGRAPHY; PREDICTIVE VALUE OF TESTS; ROC CURVE; SENSITIVITY AND SPECIFICITY; VISUAL FIELDS;

EID: 0036138639     PISSN: 01460404     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

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