Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32301
Comparing SVM and Naïve Bayes Classifier for Automatic Microaneurysm Detections

Authors: A. Sopharak, B. Uyyanonvara, S. Barman


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 Naïve 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’ hand-drawn ground-truths. The sensitivity, specificity, precision and accuracy of each method are also compared.

Keywords: Diabetic retinopathy, microaneurysm, Naïve Bayes classifier, SVM classifier.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 5984


[1] S. Wild, G. Roglic, A. Green et al., "Global prevalence of diabetes: estimates for the year 2000 and projections for 2030,” Diabetes Care 27, 2004, pp.1047-1053.
[2] P. Massin, A. Erginay, and A. Gaudric, "Retinopathie Diabetique”, Elsevier, Editions scientifiques of medicales, Elsevier, SAS, Paris 2000.
[3] T. Spencer, J.A. Olson, K.C. McHardy et al., "An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus,” Comp Biomed Res 29, 1996, pp. 284–302.
[4] M.J. Cree, J.A. Olson, K.C. McHardy et al., "A fully automated comparative microaneurysm digital detection system,” Eye 11, 1997, pp. 622–628.
[5] A. Frame, P. Undrill, M. Cree et al., "A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms,” Comput. Biol. Med. 28, 1998, pp. 225–238.
[6] T. Walter, P. Massin, A. Erginay et al., "Automatic detection of microaneurysms in color fundus images,” Medical Image Analysis 11(6), 2007, pp.555-566.
[7] C. Sinthanayothin, J.F. Boyce, T.H Williamson, T.H. et al., "Automated Detection of Diabetic Retinopathy on Digital Fundus Image,” Diabetic Medicine 19(2), 2002, pp. 105–112, 2002.
[8] D. Usher, M. Dumskyj, M. Himaga et al., "Automated Detection of Diabetic Retinopathy in Digital Retinal Images: A Tool for Diabetic Retinopathy Screening,” Diabetic Medicine 21(1), 2004, pp. 84–90.
[9] B. Dupas, T. Walter, A. Erginay et al., "Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, haemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy,” Diabetes & Metabolism 36(3), 2010, pp. 213-220.
[10] M. Niemeijer, B. van Ginneken, J. Staal et al., "Automatic detection of red lesions in digital color fundus photographs,” IEEE Trans Med Imaging 24(5), 2005, pp.584-592.
[11] B. Zhang, X. Wu, J. You et al., "Detection of microaneurysms using multi-scale correlation coefficients,” Pattern Recognition 43(6), 2010, pp. 2237-2248.
[12] B. F. Zohra and B. Mohamed, "Automated diagnosis of retinal images using SVM”, Faculte des Science, Department of Informatique, USTO, Algerie.
[13] X. Wen-Hua. "Detection of microaneurysms in bifrequency space based on SVM.” Electronics, Communications and Control (ICECC), 2011 International Conference on. IEEE, pp. 1432-1435, 2011.
[14] A. Sopharak, B. Uyyanonvara, and S. Barman., "Automatic Microaneurysm Quantification for Diabetic Retinopathy Screening.” International Conference on Image Analysis and Processing, pp. 2591-2594, 2013.
[15] A. Sopharak, B. Uyyanonvara, S. Barman et al., "Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods,” Computer Medical Imaging and Graphics 32(8), 2008, pp. 720-727.
[16] R.S. Kenneth, C.R. John, J. Matthew et al. (2006, June 15). Difference of Gaussians Edge Enhancement (Online). Available:
[17] N. Friedman, D. Geiger, and M. Goldszmidt, "Bayesian network classifiers,” Machine Learning. Vol. 29, pp.131-163, 1997.
[18] O.D. Richard, E.H. Peter, and G.S. David, Pattern Classification 2nd edition, A Wiley-Interscience Publication, 2000, pp. 20-83.
[19] X.Y. Wang, J. Garibaldi, and T. Ozen, "Application of The Fuzzy C-Means clustering Method on the Analysis of non Pre-processed FTIR Data for Cancer Diagnosis,” Internat. Conf. on Australian and New Zealand Intelligent Information Systems (ANZIIS), pp. 233-238, 2003.
[20] C.C. Change and C.J Lin. (2001). LIBSVM: A library for support vector machines (Online). Available:
[21] P.H. Chen, C.J. Lin and B. Scholkopf, "A Tutorial on v-Support Vector Machines,: Applied Stochastic Models in Business and Industry 21(2), 2005, pp. 111-136.