Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT

Acta Ophthalmologica

Volumn 88, Issue 1, 2010, Pages 44-52

  Bizios, Dimitrios ^a   Heijl, Anders ^a   Hougaard, Jesper Leth ^a   Bengtsson, Boel ^a  

^a LUND UNIVERSITY HOSPITAL   (Sweden)

Author keywords

Artificial neural networks; Glaucoma diagnosis; Machine learning; Optical coherence tomography; Retinal nerve fibre layer

Indexed keywords

ADULT; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; FEMALE; GLAUCOMA; HUMAN; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; OPEN ANGLE GLAUCOMA; OPTICAL COHERENCE TOMOGRAPHY; PIGMENT GLAUCOMA; PRIORITY JOURNAL; PSEUDOEXFOLIATION; RETINA NERVE CELL; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; VISUAL ACUITY; VISUAL FIELD;

ADULT; AGED; AREA UNDER CURVE; ARTIFICIAL INTELLIGENCE; FEMALE; GLAUCOMA; HUMANS; MALE; MIDDLE AGED; NERVE FIBERS; NEURAL NETWORKS (COMPUTER); RETINA; ROC CURVE; TOMOGRAPHY, OPTICAL COHERENCE; YOUNG ADULT;

EID: 76149109733     PISSN: 1755375X     EISSN: 17553768     Source Type: Journal    
DOI: 10.1111/j.1755-3768.2009.01784.x     Document Type: Article

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