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

Akara SOPHARAK, Bunyarit UYYANONVARA, Sarah BARMAN, Thomas WILLIAMSON

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Summary :

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.

Publication
IEICE TRANSACTIONS on Information Vol.E92-D No.11 pp.2264-2271
Publication Date
2009/11/01
Publicized
Online ISSN
1745-1361
DOI
10.1587/transinf.E92.D.2264
Type of Manuscript
PAPER
Category
Biological Engineering

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