Automated Identification of Diabetic Retinopathy Using Deep Learning

Ophthalmology

Volumn 124, Issue 7, 2017, Pages 962-969

  Gargeya, Rishab ^a   Leng, Theodore ^b  

^a Harker School   (United States)

^b STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL INTELLIGENCE; COST EFFECTIVENESS ANALYSIS; DIABETIC PATIENT; DIABETIC RETINOPATHY; DIAGNOSTIC IMAGING; DIAGNOSTIC TEST ACCURACY STUDY; HIGH RISK PATIENT; HUMAN; LEARNING ALGORITHM; OPHTHALMOLOGIST; PREDICTIVE VALUE; PRIORITY JOURNAL; RETINA EXUDATE; RETINA HEMORRHAGE; RETINA IMAGE; RETINA NEOVASCULARIZATION; SENSITIVITY AND SPECIFICITY; VISUAL IMPAIRMENT; ALGORITHM; ARTIFICIAL NEURAL NETWORK; COMPUTER ASSISTED DIAGNOSIS; MACHINE LEARNING; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; REPRODUCIBILITY; VISUAL SYSTEM EXAMINATION;

ALGORITHMS; DIABETIC RETINOPATHY; DIAGNOSTIC TECHNIQUES, OPHTHALMOLOGICAL; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MACHINE LEARNING; NEURAL NETWORKS (COMPUTER); OPHTHALMOLOGISTS; REPRODUCIBILITY OF RESULTS; ROC CURVE;

EID: 85016221341     PISSN: 01616420     EISSN: 15494713     Source Type: Journal    
DOI: 10.1016/j.ophtha.2017.02.008     Document Type: Article

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