Machine learning approach to automatic exudate detection in retinal images from diabetic patients

Journal of Modern Optics

Volumn 57, Issue 2, 2010, Pages 124-135

  Sopharak, Akara ^a   Dailey, Matthew N ^b   Uyyanonvara, Bunyarit ^a   Barman, Sarah ^c   Williamson, Tom ^d   Nwe, Khine Thet ^b   Moe, Yin Aye ^b  

^a THAMMASAT UNIVERSITY   (Thailand)

^b ASIAN INSTITUTE OF TECHNOLOGY   (Thailand)

^c KINGSTON UNIVERSITY   (United Kingdom)

^d St Thomas   (United Kingdom)

Author keywords

Diabetic retinopathy; Exudate; Naive Bayes classifier; Nearest neighbour classifier; Support vector machine

Indexed keywords

CLASSIFICATION PERFORMANCE; DIABETIC PATIENT; DIABETIC RETINOPATHY; EARLY DETECTION; FEATURE SELECTION; FEATURE SETS; FIRST FIT; GRID SEARCH; HYPERPARAMETERS; MACHINE-LEARNING; NAIVE BAYES; NAIVE BAYES CLASSIFIER; NAIVE BAYES CLASSIFIERS; NAIVE BAYES MODELS; NEAREST NEIGHBOUR; NEAREST-NEIGHBOUR CLASSIFIER; NEGATIVE EXAMPLES; POSITIVE EXAMPLES; RADIAL BASIS FUNCTIONS; RETINAL IMAGE; SVM CLASSIFIERS; TRAINING ERRORS; TRAINING SETS; VISION LOSS;

CLASSIFIERS; EYE PROTECTION; IMAGE ENHANCEMENT; RADIAL BASIS FUNCTION NETWORKS; SUPPORT VECTOR MACHINES;

FEATURE EXTRACTION;

EID: 77951229367     PISSN: 09500340     EISSN: 13623044     Source Type: Journal    
DOI: 10.1080/09500340903118517     Document Type: Article

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