Diabetic Retinopathy Fundus Image Classification Using Self-Organizing Maps
Downloads
Background: Diabetic retinopathy (DR) is a condition that impairs the blood vessels in the retina, resulting in vision loss ranging from temporary to permanent blindness. It commonly affects individuals diagnosed with diabetes mellitus (DM). Fundoscopy is a technique used to identify DR by examining the fundus of the eye during an eye examination. This process is time-consuming and can be expensive.
Objective: This study aimed to examine the identification of DR using digital image processing methods.
Methods: The self-organizing map (SOM) artificial neural network was employed. Diabetic retinopathy will be categorized according to its severity, including normal, mild, moderate, or severe. This classification considers the quantity of exudates and microaneurysms and the blood vessel structure in the fundus image. The dataset used in this investigation comprised 1000 fundus images acquired from the MESSIDOR ophthalmology database.
Results: The findings indicate that the SOM approach achieves a training accuracy of 72% and a testing accuracy of 62%.
Conclusion: The DR severity classification system can effectively extract DR-related features by segmenting exudates, blood vessels, and microaneurysms from funduscopic images during training, testing, and evaluation.
Keywords: Diabetic Retinopathy, Self-Organizing Map, Fundus Image Classification, Digital Image Processing
W. L. Yun and M. R. K. Mookiah, “Detection of diabetic retinopathy using K-means clustering and self-organizing map,” J. Med. Imaging Health Informatics, vol. 3, no. 4, pp. 575–581, 2013, doi: 10.1166/jmihi.2013.1207.
Statista Research Department, “Countries with the highest number of diabetics worldwide in 2021.” [Online]. Available: https://www.statista.com/statistics/281082/countries-with-highest-number-of-diabetics/ (accessed Feb. 27, 2025).
S. D. Solomon et al., “Diabetic Retinopathy: A Position Statement by the American Diabetes Association,” Diabetes Care, vol. 40, no. 3, pp. 412–418, 2017, doi: 10.2337/dc16-2641.
N. Tsiknakis et al., "Deep learning for diabetic retinopathy detection and classification based on fundus images: A review," Computers in Biology and Medicine, vol. 139, p. 104599, 2021. doi: 10.1016/j.compbiomed.2021.104599.
Z. L. Teo, Y. C. Tham, M. Yu, M. L. Chee, T. H. Rim, N. Cheung, et al., “Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045: Systematic Review and Meta-analysis,” Ophthalmology, vol. 128, no. 11, pp. 1580–1591, 2021.
P. Soewondo et al., “Outcomes on control and complications of type 2 diabetic patients in Indonesia,” Med. J. Indones., vol. 19, no. 4, pp. 235–244, 2010, doi: 10.13181/mji.v19i4.412.
O. Deperlioğlu and U. Köse, “Diagnogsis of Diabetic Retinopathy Using Image Processing and Convolutional Neural Network,” in Proc. Med. Technol. Nat. Congr. (TIPTEKNO), 2018.
S. Lu, J. Liu, M. Liu, and Q. Zhao, “A Diabetic Retinopathy Classification Method Based on Improved InceptionV3 Neural Network Model,” in Proc. 35th Chin. Control Decis. Conf. (CCDC), 2023.
V. Basreddy and S. Veerashetty, “Preprocessing, Feature Extraction, and Classification Methodologies on Diabetic Retinopathy Using Fundus Images,” in Proc. Int. Conf. Integr. Intell. Commun. Syst. (ICIICS), 2023.
Y. S. Boral and S. S. Thorat, “Classification of Diabetic Retinopathy Based on Hybrid Neural Network,” in Proc. 5th Int. Conf. Comput. Methodol. Commun. (ICCMC), 2021.
H. Z. R. Eduardo, V. L. Yensi, J. F. M. Escalona, and M. D. Ruiz, “Strategy for the Detection of Types of Eye Diseases Using SOM Neural Network,” in Proc. LACCEI Int. Multi-Conf. Eng. Educ. Technol., 2022.
A. Holzinger, A. Carrington, and H. Müller, “Measuring the quality of explanations: The system causability scale (SCS),” KI - Künstliche Intelligenz, vol. 34, no. 2, pp. 193–198, 2020.
T. Kohonen, Self-Organizing Maps, 3rd ed. Berlin, Germany: Springer, 2001.
V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, et al., “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA, vol. 316, no. 22, pp. 2402–2410, 2016, doi: 10.1001/jama.2016.17216.
T. Murakami, N. Ueda-Arakawa, K. Nishijima, T. Akagi, A. Uji, T. Horii, and N. Yoshimura, “Macular morphological patterns in diabetic retinopathy by self-organizing map,” Invest. Ophthalmol. Vis. Sci., vol. 53, no. 14, pp. 2851–2851, 2012.
C. A. Lupaşcu and D. Tegolo, “Automatic unsupervised segmentation of retinal vessels using self-organizing maps and k-means clustering,” in Proc. Int. Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB), Berlin, Germany: Springer, 2010, pp. 263–274
J. Decencière et al., “Feedback on a publicly distributed database: the Messidor database,” Image Anal. Stereol., vol. 33, no. 3, pp. 231–234, Aug. 2014, doi: 10.5566/ias.1155.
C. P. Wilkinson, F. L. Ferris III, R. E. Klein, P. P. Lee, C. D. Agardh, M. Davis, R. D. Dills, A. Kampik, G. Pararajasegaram, and J. T. Verdaguer, "Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales," Ophthalmology, vol. 110, no. 9, pp. 1677–1682, Sep. 2003, doi: 10.1016/S0161-6420(03)00475-5.
I. R. Dewi, I. R. Magdalena, and R. Y. Nur, “Klasifikasi Retinopati Diabetik Pada Citra Mata Digital Menggunakan 3D Glcm Dengan Learning Vector Quantization,” J. Inform., vol. 6, no. 2, pp. 4186–4193, 2019.
D. Sn, “Detection of Diabetic Retinopathy Using Kirsch Edge Detection and Watershed Transformation Algorithm,” Int. J. Adv. Res. Ideas Innov. Technol., vol. 1, no. 2, 2015.
Z. Gu, Z. Chen, H. Song, C. Chen, and Y. Yang, "Classification of Diabetic Retinopathy Severity in Fundus Images Using the Vision Transformer and Residual Attention," Computational and Mathematical Methods in Medicine, vol. 2023, Article ID 9831706, 11 pages, 2023.
S. Joshi and P. T. Karule, "Mathematical morphology for microaneurysm detection in fundus images," European Journal of Ophthalmology, vol. 30, no. 5, pp. 1135–1142, Sep. 2020, doi: 10.1177/1120672119843021.
R. Haldar, S. Aruchamy, A. Chatterjee, and P. Bhattacharjee, “Diabetic Retinopathy Image Enhancement Using Vessel Extraction in Retinal Fundus Images by Programming in Raspberry Pi Controller Board,” in Int. Conf. Interdiscip. Res. Eng. Technol., vol. 20, no. 5, pp. 21–23, 2016, doi: 10.1108/14777280610688014.
E. Technologies, “SOMatic Viewer: Implementation of an Interactive Self-Organizing Map Visualization Toolset in Processing and Java,” Master’s thesis, 2013.
L. Yang, Z. Ouyang, and Y. A. Shi, “A Modified Clustering Method Based on Self-Organizing Maps and Its Applications,” Procedia Comput. Sci., 2012.
H. Elghazel and K. Benabdeslem, “Different Aspects of Clustering the Self-Organizing Maps,” Neural Process. Lett., 2014.
H. Elghazel and K. Benabdeslem, “Towards B-coloring of SOM,” Lect. Notes Comput. Sci., vol. 2009, pp. 322–336, 2009.
J. Liu and L. Xu, “Improvement of SOM Classification Algorithm and Application Effect Analysis in Intrusion Detection,” Adv. Intell. Syst. Comput., 2019.
A. S. Danielsen et al., “Self‑Organizing Maps for Clustering Hyperspectral Images,” Remote Sensing, vol. 13, no. 20, 2021.
E. Sabrina, "Klasifikasi Penyakit Diabetic Retinopathy Menggunakan Metode Learning Vector Quantization (LVQ)," Jurnal Teknik Elektro, vol. 6, no. 2, 2017.
L. Heryawan, "Deteksi Dini Retinopati Diabetik dengan Pengolahan Citra Berbasis Morfologi Matematika," IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 11, no. 2, p. 209, 2017.
Copyright (c) 2025 The Authors. Published by Universitas Airlangga.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
All accepted papers will be published under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain copyright and grant the journal right of first publication. CC-BY Licenced means lets others to Share (copy and redistribute the material in any medium or format) and Adapt (remix, transform, and build upon the material for any purpose, even commercially).
