Assessment of four neural network based classifiers to automatically detect red lesions in retinal images

Medical Engineering and Physics

Volumn 32, Issue 10, 2010, Pages 1085-1093

  García, María ^a   López, María I ^b   Álvarez, Daniel ^a   Hornero, Roberto ^a  

^a UNIVERSITY OF VALLADOLID   (Spain)

^b UNIVERSITY OF VALLADOLID   (Spain)

Author keywords

Diabetic retinopathy; Logistic regression; Neural network; Red lesion; Retinal imaging

Indexed keywords

AUTOMATIC DETECTION; COMPLEXITY CRITERIA; DIABETIC RETINOPATHY; FEATURE SELECTION; IMAGE REGIONS; IMAGE-BASED; LOGISTIC REGRESSION; LOGISTIC REGRESSIONS; MAJORITY VOTING; MEAN SENSITIVITY; MICROANEURYSMS; MULTI LAYER PERCEPTRON; POSITIVE PREDICTIVE VALUES; RADIAL BASIS FUNCTIONS; RED LESIONS; RETINAL IMAGE; RETINAL IMAGING; SHAPE FEATURES; TEST SETS; TRAINING SETS; VISUAL IMPAIRMENT;

CLASSIFIERS; DIAGNOSIS; EYE PROTECTION; FEATURE EXTRACTION; IMAGE ENHANCEMENT; OPHTHALMOLOGY; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; CLASSIFIER; COMPUTER ASSISTED DIAGNOSIS; DATA BASE; DIAGNOSTIC ACCURACY; IMAGE ANALYSIS; LOGISTIC REGRESSION ANALYSIS; PRACTICE GUIDELINE; PRIORITY JOURNAL; RETINA DISEASE; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

COLOR; DIABETIC RETINOPATHY; IMAGE ENHANCEMENT; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; RETINA; SENSITIVITY AND SPECIFICITY;

EID: 78449260702     PISSN: 13504533     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.medengphy.2010.07.014     Document Type: Article

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