Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks

JAMA Ophthalmology

Volumn 135, Issue 11, 2017, Pages 1170-1176

  Burlina, Philippe M ^a   Joshi, Neil ^a   Pekala, Michael ^a   Pacheco, Katia D ^b   Freund, David E ^a   Bressler, Neil M ^c  

^a JOHNS HOPKINS UNIVERSITY APPLIED PHYSICS LABORATORY   (United States)

^b Vision Eye Hospital   (Brazil)

^c JOHNS HOPKINS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AGE RELATED MACULAR DEGENERATION; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; DEEP CONVOLUTIONAL NEURAL NETWORK; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DIET SUPPLEMENTATION; EYE FUNDUS; EYE PHOTOGRAPHY; GOLD STANDARD; HUMAN; INTERMETHOD COMPARISON; MAJOR CLINICAL STUDY; PREDICTIVE VALUE; PRIORITY JOURNAL; RISK REDUCTION; SENSITIVITY AND SPECIFICITY; SUBRETINAL NEOVASCULARIZATION; VISUAL IMPAIRMENT; COMPARATIVE STUDY; MACHINE LEARNING; RECEIVER OPERATING CHARACTERISTIC; REPRODUCIBILITY; WET MACULAR DEGENERATION;

ALGORITHMS; FUNDUS OCULI; HUMANS; MACHINE LEARNING; NEURAL NETWORKS (COMPUTER); REPRODUCIBILITY OF RESULTS; ROC CURVE; WET MACULAR DEGENERATION;

EID: 85034636594     PISSN: 21686165     EISSN: None     Source Type: Journal    
DOI: 10.1001/jamaophthalmol.2017.3782     Document Type: Article

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