Sistem Klasifikasi Tingkat Keparahan Retinopati Diabetik Menggunakan Support Vector Machine
Taufiq Galang Adi Putranto(1*), Ika Candradewi(2)
(1) Prodi Elektronika dan Instrumentasi, DIKE, FMIPA, UGM, Yogyakarta, Indonesia
(2) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
Diabetic retinopathy is a vision disorder disease that can cause damage to the retina of the eye that will have a direct impact on the disruption of vision of the patient. The diabetic retinopathy phase is classified into four types (normal, mild NPDR, moderate NPDR (Non-Proliferative Diabetic Retinopathy), and severe NPDR). Retinal of eye data of diabetic retinopathy patients treated from the MESSIDOR database. By applying image processing, the retinal image of the eye in extraction using the area features extraction from the detection of exudate, blood vessels, microaneurysms, and texture feature extraction Gray Level Co-occurrence Matrix. The extracted results classified using the Support Vector Machine method with the Radial Basis Function (RBF) kernel. Classification evaluated with these parameters: Accuracy, specificity, and sensitivity.
The results of classification show the best value using 6 statistical features ie, contrast, homogeneity, correlation, energy, entropy and inverse difference moment in the direction of 45 degrees with the RBF kernel. The result of classification research system on 240 data training and 60 data testing yields an average accuracy is 95.93%, the value of specificity is 97.29%, and a sensitivity rating is 91.07%. From the research result, using RBF kernel get the best accuracy result than using kernel polynomial or kernel linear.
Keywords
Full Text:
PDFReferences
[1] Yuhelma, Y. Hasneli and F.A. Nauli, ”Identifikasi Dan Analisis Komplikasi Makrovaskuler dan Mikrovaskuler pada Pasien Diabetes Mellitus,” Universitas Riau, 2014 [Online]. Avaible: https://jom.unri.ac.id/index.php/JOMPSIK/article/view/8343/8012. [Accesed: 17-April-2017]
[2] American Diabetes Association, “Diagnosis and Classification of Diabetes Mellitus,” Diabetes Care, vol. 36, Supplement 1, Jan. 2013 [Online]. Available: https://doi.org/10.2337/dc13-S067. [Accesed: 20-April-2017]
[3] I. M. Stratton, S.J. Aldington, D.J. Taylor, A.I. Adler, and P.A. Scanlon, “A simple Risk Stratification for Time to Development of Sight-Threatening Diabetic Retinopathy,” Diabetes Care, vol. 36, p. 580, Mar. 2013 [Online]. Available: https://doi.org/10.2337/dc12-0625. [Accesed: 21-April-2017]
[4] M. Kuivalainen, “Retinal Image Analysis Using Machine Vision,” M.S. thesis, Department of Information Technology., Lappeenranta University of Technology., Finlandia., M.B, 2005.
[5] P. R. Singh and M. Dixit, “Histogram Equalization: A Strong Technique for Image Enhancement,” IJSIP (International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol.8, No.8, pp. 345-352, 2015 [Online]. Available: www.sersc.org/journals/IJSIP/vol8_no8/35.pdf. [Accesed: 10-May-2017]
[6] P. S. Venugopala, H. Sarojadevi, A. Ankitha, and N. Niranjan, “ An Approach to Improve Canny Edge Detection Using Morphological Filters,” IJCA (International Journal of Computer Application, Vol.116, No. 9, Apr. 2015 [Online]. Available: http://www.ijcaonline.org/archives/volume116/number9/20368-2575. [Accesed: 12-May-2017]
[7] A. Elbalaoui, M. Fakir, K. Taifi, and A. Merbouha, “ Automatic Detection of Blood Vessel in Retinal Images,” International Conference Computer Graphics Imageing and Visualization, Vol.12, Issue.1, pp. 14-29, 2017 [Online]. Available: https://doi.org/10.4018/IJHISI.2017010102. [Accesed: 13-May-2017]
[8] R. Yefrenes and A. Harjoko, “Klasifikasi Fase Retinopati Diabetes Menggunakan Backpropagation Neural Network,” IJEIS (Indonesian Journal of Electronics and Instrumentations Systems, Vol.1, No.2, 2011 [Online]. Available: https://jurnal.ugm.ac.id/ijeis/article/view/1966/1771. [Accesed: 21-May-2017]
[9] T. Ruba, and K. Ramalakshmi, “Identification and Segmentation of Exudates Using SVM Classifier,” in International Conference on Innovations in Information Embedded and Comunication System, 2015 [Online]. Available: https://doi.org/10.1109/ICIIECS.2015.7193219. [Accesed: 3-July-2017]
[10] X. Zhang, G. Thibault, E. Decencière, B. Marcotegui, B. Laÿ, R. Danno, G. Cazuguel, G. Quellec, M. Lamard, P. Massin, A. Chabouis, Z. Victor, and A. Erginay, “Exudate Detection in Color Retinal Images for Mass Screening of Diabetic Retinopathy,” Medical Image Analysis, 2014 [Online]. Available: http://dx.doi.org/10.1016/j.media.2014.05.004. [Accesed: 17-july-2017]
DOI: https://doi.org/10.22146/ijeis.31206
Article Metrics
Abstract views : 3378 | views : 11049Refbacks
- There are currently no refbacks.
Copyright (c) 2018 IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1