WASET

	%0 Journal Article
	%A A. Sopharak and  B. Uyyanonvara and  S. Barman 
	%D 2014
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 89, 2014
	%T Comparing SVM and Naïve Bayes Classifier for Automatic Microaneurysm Detections 
	%U https://publications.waset.org/pdf/9998289
	%V 89
	%X Diabetic retinopathy is characterized by the development of retinal microaneurysms. The damage can be prevented if disease is treated in its early stages. In this paper, we are comparing Support Vector Machine (SVM) and Na&iuml;ve Bayes (NB) classifiers for automatic microaneurysm detection in images acquired through non-dilated pupils. The Nearest Neighbor classifier is used as a baseline for comparison. Detected microaneurysms are validated with expert ophthalmologists&rsquo; hand-drawn ground-truths. The sensitivity, specificity, precision and accuracy of each method are also compared.

	%P 797 - 800