Feature Extraction Analysis for Diabetic Retinopathy Detection Using Machine Learning Techniques

Loneli Costaner, Lisnawita Lisnawita, Guntoro Guntoro, Abdullah Abdullah

Abstract


Diabetic retinopathy is a serious complication of diabetes that can lead to blindness if not detected and treated early. Automated detection of diabetic retinopathy requires effective feature extraction techniques to enhance diagnostic accuracy. This study aims to develop a method for detecting diabetic retinopathy by utilizing Local Binary Pattern (LBP) combined with wavelet transform, and then classifying the extracted features using Support Vector Machine (SVM). The approach includes feature extraction from retinal images using LBP and wavelet transform. The extracted features are subsequently classified with SVM to evaluate performance in detecting diabetic retinopathy. Analysis results show that the dominant feature is found in the fifth row with a value of 0.57006, indicating the effectiveness of the LBP method in feature extraction. The developed model demonstrates high performance with an accuracy of 95.59%, precision of 96%, recall of 97.96%, and F1-score of 96.97%. The combination of feature extraction methods with SVM proves to be effective and reliable in detecting diabetic retinopathy, offering low error rates and high accuracy, thus potentially serving as a valuable tool in clinical diagnosis

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References


E. Abdelmaksoud, S. El-sappagh, S. Barakat, T. Abuhmed, and M. Elmogy, “Automatic Diabetic Retinopathy Grading System Based on Detecting Multiple Retinal Lesions,” IEEE Access, no. Vl, pp. 15939–15960, 2021, doi: 10.1109/ACCESS.2021.3052870.

N. Mazlan, H. Yazid, H. Arof, and H. Mohd, “Automated Microaneurysms Detection and Classification using Multilevel Thresholding and Multilayer Perceptron,” J. Med. Biol. Eng., no. 0123456789, 2020, doi: 10.1007/s40846-020-00509-8.

D. Parashar and D. K. Agrawal, “Automated Classification of Glaucoma Stages Using Flexible Analytic Wavelet Transform From Retinal Fundus Images,” IEEE, vol. 1748, no. c, 2020, doi: 10.1109/JSEN.2020.3001972.

M. Jebran P and S. Gupta, “Microaneurysm detection by multiple feature subset extraction and selection based on SVM-weights and Genetic Algorithm-Neural Network,” Int. Conf. Adv. Comput. Commun. Syst., pp. 129–134, 2021, doi: 10.1109/ICACCS51430.2021.9441746.

M. Miron, A. Culea-florescu, and S. Moldovanu, “Diabetic Retinopathy Image Classification Using Machine Learning and Local Binary Patterns Features,” 2023 8th Int. Symp. Electr. Electron. Eng., pp. 136–139, 2023, doi: 10.1109/ISEEE58596.2023.10310398.

D. J. Derwin, S. T. Selvi, and O. J. Singh, “Discrimination of microaneurysm in color retinal images using texture descriptors,” Signal, Image Video Process., vol. 14, no. 2, pp. 369–376, 2020, doi: 10.1007/s11760-019-01566-6.

B. B. Narhari, B. K. Murlidhar, A. D. Sayyad, and G. S. Sable, “Automated diagnosis of diabetic retinopathy enabled by optimized thresholding-based blood vessel segmentation and hybrid classifi er,” Bio-Algorithms and Med-Systems, vol. 17, no. 1, pp. 9–23, 2021.

N. Matthew, F. Kwan, A. Dewi, and F. Indra, “Comparing Local Local Binary Binary Pattern Pattern and and Gray Gray Level Level Co-occurrence Matrix for Feature Extraction in Diabetic Retinopathy Classification Matrix for Feature Extraction in Diabetic Retinopathy Classificati,” Procedia Comput. Sci., vol. 227, pp. 355–363, 2023, doi: 10.1016/j.procs.2023.10.534.

P. K. Muhuri, Z. Ashraf, and S. Goel, “A Novel Image Steganographic Method based on Integer Wavelet Transformation and Particle Swarm Optimization,” Appl. Soft Comput. J., vol. 92, p. 106257, 2020, doi: 10.1016/j.asoc.2020.106257.

C. Tian, M. Zheng, W. Zuo, B. Zhang, Y. Zhang, and D. Zhang, “Multi-stage image denoising with the wavelet transform,” Sci. Pattern, vol. 134, 2023.

H. A. Agboola and J. E. Zaccheus, “Wavelet image scattering based glaucoma detection,” BMC Biomed. Eng., pp. 1–11, 2023, doi: 10.1186/s42490-023-00067-5.

S. Gayathri, V. P. Gopi, and P. Palanisamy, “Automated classification of diabetic retinopathy through reliable feature selection,” Phys. Eng. Sci. Med., vol. 43, no. 3, pp. 927–945, 2020, doi: 10.1007/s13246-020-00890-3.

R. K. Patel and M. Kashyap, “Automated screening of glaucoma stages from retinal fundus images using BPS and LBP based GLCM features,” Int. J. Imaging Syst. Technol., vol. 33, no. 1, pp. 246–261, 2022, doi: 10.1002/ima.22797.

R. Tyasnurita and A. M. Y. Pamungkas, “Deteksi Diabetik Retinopati menggunakan Regresi Logistik,” Ilk. J. Ilm., vol. 12, no. 2, pp. 130–135, 2020.

Y. F. Rachman, K. Kusrini, and H. Alfatta, “Klasifikasi Citra Digitalretina Penderita Diabetes Retinopati Menggunakan Metode Euclidean,” DoubleClick J. Comput. Inf. Technol., vol. 3, no. 2, pp. 75–82, 2020.

R. J. S. Reubun, C. L. Tamtellahiru, and M. Yunita, “Prevalensi Retinopati Diabetik Pada Penderita Diabetes Melitus Di Klinik Utama Provinsi Maluku,” Care J. Ilm. Ilmu Kesehat., vol. 10, no. 3, pp. 366–376, 2022.

T. P. H. Nissen et al., “Performance of a Support Vector Machine Learning Tool for Diagnosing Diabetic Retinopathy in Clinical Practice,” J. Pers. Med., pp. 1–13, 2023.

I. Bibi, J. Mir, and G. Raja, “Automated detection of diabetic retinopathy in fundus images using fused features,” Phys. Eng. Sci. Med., vol. 43, no. 4, pp. 1253–1264, 2020, doi: 10.1007/s13246-020-00929-5.

J. Gao, B. Wang, Z. Wang, Y. Wang, and F. Kong, “A wavelet transform-based image segmentation method,” Optik (Stuttg)., vol. 208, no. December 2019, p. 164123, 2020, doi: 10.1016/j.ijleo.2019.164123.

M. A. I. Mahmood, N. Aktar, and M. F. Kader, “A hybrid approach for diagnosing diabetic retinopathy from fundus image exploiting deep features,” Heliyon, vol. 9, no. 9, pp. 1–14, 2023, doi: 10.1016/j.heliyon.2023.e19625.




DOI: https://doi.org/10.32520/stmsi.v13i5.4600

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