Comparative Analysis of Automatic Exudate Detection between Machine Learning and Traditional Approaches


IEICE TRANSACTIONS on Information and Systems   Vol.E92-D   No.11   pp.2264-2271
Publication Date: 2009/11/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E92.D.2264
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Biological Engineering
exudate,  diabetic retinopathy,  morphological,  fuzzy c-means,  naive Bayesian classifier,  support vector machine,  nearest neighbor classifier,  

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To prevent blindness from diabetic retinopathy, periodic screening and early diagnosis are neccessary. Due to lack of expert ophthalmologists in rural area, automated early exudate (one of visible sign of diabetic retinopathy) detection could help to reduce the number of blindness in diabetic patients. Traditional automatic exudate detection methods are based on specific parameter configuration, while the machine learning approaches which seems more flexible may be computationally high cost. A comparative analysis of traditional and machine learning of exudates detection, namely, mathematical morphology, fuzzy c-means clustering, naive Bayesian classifier, Support Vector Machine and Nearest Neighbor classifier are presented. Detected exudates are validated with expert ophthalmologists' hand-drawn ground-truths. The sensitivity, specificity, precision, accuracy and time complexity of each method are also compared.