Detecting Lesion Characteristics of Diabetic Retinopathy Using Machine Learning and Computer Vision

Alhadi Bustamam, Devvi Sarwinda, Bariqi Abdillah, Tesdiq Prigel Kaloka

Abstract


One indicator of the severity of diabetic retinopathy is the existence of lesion characteristics in the eyes such as microaneurysm, hemorrhages, exudates, and neovascularization. Without proper early medical attention, this lesion could lead to blindness. Considering its importance, a system that could detect such lesions will be beneficial. This paper investigates the lesion characteristics of diabetic retinopathy from fundus images such as microaneurysm (redsmalldots), exudates, hemorrhages, and neovascularization. In this study, we present three of feature extraction methods, i.e., Local Binary Pattern (LBP), Gray Level Co-Occurrence Matrix (GLCM) and Segmentation Fractal Texture Analysis (SFTA). K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) chosen as classifiers for classifying five classes (redsmalldots, hemorrhages, hard exudates, soft exudates, and neovascularization). The data used in this research obtained from DiaretDB0 database. The experimental results show that our proposed method can detect the lesion characteristics of diabetic retinopathy with higher accuracy of 86,84% and 96% for SVM and KNN, respectively.

Keywords


diabetic retinopathy; LBP; GLCM; SFTA; KNN; SVM

Full Text:

PDF

References


Yau J.W, Rogers S.L, et al., “Global prevalence and major risk factors of diabetic retinopathy,†Diabetes Care; 35(3):556-64, 2012.

M. Usman Akram, S. Khalid, A. Tariq, S.A. Khan, and F. Azam, “Detection and classification of retinal lesions for grading of diabetic retinopathy,†Computers in Biology and Medicine, 45(1), 161–171, 2014.

K. Narasimhan, V.C. Neha and K. Vijayarekha, “An Efficient Automated System for Detection of Diabetic Retinopathy from Fundus Images Using Support Vector Machine and Bayesian Classifiers,†in IEEE Transl. International Conference on Computing, Electronics and Electrical Technologies (ICCEET), Kumaracoil, 2012. pp. 964-969.

S. Ravishankar, A. Jain, and A. Mittal, “Automated Feature Extraction for Early Detection of Diabetic Retinopathy in Fundus Images,†in IEEE Transl. International Conference on Computer Vision and Pattern Recognition (CVPR), 2009, pp.210-217.

V.V. Kumari, N.Suriyanarayanan and C.Thanka Saranya, â€Feature Extraction for Early Detection of Diabetic Retinopathy,†in IEEE Transl. International Conference on Recent Trends in Information, Telecommunication and Computing, 2010, pp. 359-361.

B. Abdillah, A. Bustamam, and S. Devvi, “Classification of Diabetic Retinopathy Through Texture Features Analysis,†in 2017 International Conference on Advanced Computer Science and Information Systems, Bali, Indonesia, 2017, pp. 333-337.

K. Ram, G.D. Joshi, and J. Sivaswamy, “A successive clutter-rejection-based approach for early detection of diabetic retinopathy,†in IEEE Transactions on Biomedical Engineering, 2011, 58(3 PART 1), 664–673.

T. Kauppi, V. Kalesnykiene, J. Kamarainen, L. Lensu, I. Sorri, H. Uusitalo, H. Kalviainen and J. Pietila, “DIARETDB0: Evaluation Database and Methodology for Diabetic Retinopathy Algorithms,†Technical report.

Datta, N. S., Dutta, H. S., De, M., & Mondal, S. “An Effective Approach: Image Quality Enhancement for Microaneurysms Detection of Non-dilated Retinal Fundus Image.†Procedia Technology. 2013; 10, 731–737

T. Ojala, M. Pietikainen and D. Harwood, “A Comparative Study of Texture Measures with Classification Based on Feature Distributions,†Pattern Recognition, Vol. 29, No. 1, pp. 51-59, 1996.

D. Sarwinda and A. Bustamam, “Detection of Alzheimer’s disease using advanced local binary pattern from hippocampus and whole brain of MR images,†in 2016 International Joint Conference on Neural Networks (IJCNN), 2016, 5051–5056.

K. Meena and A. Suruliandi, “Local Binary Patterns and its Variants for face Recognition,†in IEEE-International Conference on Recent Trends in Information Technology (ICRTIT), 2011, Chennai, 782-786.

T. Ahonen, A. Hadid and M. Pietikainen, "Face Description with Local Binary Patterns: Application to Face Recognition," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037-2041, Dec. 2006.

Xie, Z. X. Z., Liu, G. L. G., He, C. H. C., and Wen, Y. W. Y. “Texture Image Retrieval Based on Gray Level Co-Occurrence Matrix and Singular Value Decomposition,†in 2010 International Conference on Multimedia Technology (ICMT), 2010, (1), 3–5.

A.F. Costa, G. Humpire-Mamani, and A.J.M. Traina, “An efficient algorithm for fractal analysis of textures,†Brazilian Symposium of Computer Graphic and Image Processing, 2012, 39–46.

S. Mirajkar and M.M. Patil, “Feature Extraction of Diabetic Retinopathy Imagesâ€, in International Journal of Computer Applications® (IJCA) (0975 – 8887) Proceedings on Emerging Trends in Electronics and Telecommunication Engineering (Ncet), 2013, 5–8.

A.T, Nasser and N. Dogru, “Signature recognition by using SIFT and SURF with SVM basic on RBF for voting online,†in 2017 International Conference on Engineering and Technology (ICET), 2017. Antalya, Turkey.




DOI: http://dx.doi.org/10.18517/ijaseit.10.4.8876

Refbacks

  • There are currently no refbacks.


Published by INSIGHT - Indonesian Society for Knowledge and Human Development