Increasing the reliability of diagnosis of diabetic retinopathy based on machine learning

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.297849

Keywords:

fundus images, diabetic retinopathy, neural network, image preprocessing, medical image analysis

Abstract

This paper discusses the method of measuring and analyzing the parameters of the retina with subsequent diagnosis based on them of pathological changes due to diabetic retinopathy, which is crucial in the field of medicine to help doctors in timely detection and treatment of the disease. The main problem of biomedical image data analysis is insufficient pre-processing of images for further clear determination of informative indicators. This paper explores the application of machine learning and image processing techniques to develop an effective method for the diagnosis of diabetic retinopathy. The main focus is on obtaining the optimal model using machine learning and different types of neural networks. This paper considered and analyzed such methods of image preprocessing as: median filtering, grayscale conversion, cropping of non-informative areas of the image, selection of contours. The classification results of three rules (Classical Neural Networks (CNNs), Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) were analyzed, and through experimental studies it was determined that the ANN performed the task best (accuracy=87.1 %, reliability=84.6 %, sensitivity=91.6 %, specificity=84 %). An information model was obtained to support decision-making in assessing the condition of the retina using the processing of the obtained microscopic images and further analysis of informative parameters, and a database of more than 35,000 samples and informative features of the retina was formed. Given the sufficient quality of classification and the availability of software and hardware, this method can be developed and applied in practice in medical institutions after conducting all the necessary clinical studies

Author Biographies

Orken Mamyrbayev, U. Joldasbekov Institute of Mechanics and Engineering

Doctor PhD, Associate Professor

Department of Artificial Intelligence

Sergii Pavlov, Vinnytsia National Technical University

Doctor of Technical Sciences, Professor

Department of Biomedical Engineering and Optic-Electronic Systems

Oleksandr Karas, Vinnytsia National Technical University

Doctor PhD

Yosip Saldan, National Pirogov Memorial Medical University

Doctor of Medical Sciences, Professor

Department of Eye Diseases

Kymbat Momynzhanova, Al-Farabi Kazakh National University

Postgraduate Student

Department of Information Systems

Sholpan Zhumagulova, Al-Farabi Kazakh National University

Postgraduate Student

Department of Artificial Intelligence and Big Data

References

  1. Diabetes. WHO. Available at: https://www.who.int/health-topics/diabetes#tab=tab_1
  2. Burton, M. J., Ramke, J., Marques, A. P., Bourne, R. R. A., Congdon, N., Jones, I. et al. (2021). The Lancet Global Health Commission on Global Eye Health: vision beyond 2020. The Lancet Global Health, 9 (4), e489–e551. https://doi.org/10.1016/s2214-109x(20)30488-5
  3. GBD Results. Institute for Health Metrics and Evaluation. Available at: https://vizhub.healthdata.org/gbd-results/
  4. Diabetes Now Affects One in 10 Adults Worldwide. International Diabetes Federation. Available at: https://idf.org/news/diabetes-now-affects-one-in-10-adults-worldwide/
  5. Kropp, M., Golubnitschaja, O., Mazurakova, A., Koklesova, L., Sargheini, N., Vo, T.-T. K. S. et al. (2023). Diabetic retinopathy as the leading cause of blindness and early predictor of cascading complications – risks and mitigation. EPMA Journal, 14 (1), 21–42. https://doi.org/10.1007/s13167-023-00314-8
  6. Bhatwadekar, A. D., Shughoury, A., Belamkar, A., Ciulla, T. A. (2021). Genetics of Diabetic Retinopathy, a Leading Cause of Irreversible Blindness in the Industrialized World. Genes, 12 (8), 1200. https://doi.org/10.3390/genes12081200
  7. Chabba, N., Silwal, P. R., Bascaran, C., McCormick, I., Goodman, L., Gordon, I. et al. (2024). What is the coverage of retina screening services for people with diabetes? Protocol for a systematic review and meta-analysis. BMJ Open, 14 (1), e081123. https://doi.org/10.1136/bmjopen-2023-081123
  8. Jonas, J. B., Sabanayagam, C. (2019). Epidemiology and Risk Factors for Diabetic Retinopathy. Diabetic Retinopathy and Cardiovascular Disease, 20–37. https://doi.org/10.1159/000486262
  9. Ursin, F., Timmermann, C., Orzechowski, M., Steger, F. (2021). Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent? Frontiers in Medicine, 8. https://doi.org/10.3389/fmed.2021.695217
  10. Artificial Neural Networks/Neural Network Basics. Wikibooks. Available at: https://en.wikibooks.org/wiki/Artificial_Neural_Networks/Neural_Network_Basics
  11. Diabetic Retinopathy Detection. Kaggle. Available at: https://www.kaggle.com/c/diabetic-retinopathy-detection/data
  12. Li, T., Gao, Y., Wang, K., Guo, S., Liu, H., Kang, H. (2019). Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Information Sciences, 501, 511–522. https://doi.org/10.1016/j.ins.2019.06.011
  13. Decencière, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C. et al. (2014). Feedback on a publicly distributed image database: the messidor database. Image Analysis & Stereology, 33 (3), 231. https://doi.org/10.5566/ias.1155
  14. Li, W., Bian, L., Ma, B., Sun, T., Liu, Y., Sun, Z. et al. (2024). Interpretable Detection of Diabetic Retinopathy, Retinal Vein Occlusion, Age-Related Macular Degeneration, and Other Fundus Conditions. Diagnostics, 14 (2), 121. https://doi.org/10.3390/diagnostics14020121
  15. Kumar, N. S., Balasubramanian, R. K., Phirke, M. R. (2023). Image Transformers for Diabetic Retinopathy Detection from Fundus Datasets. Revue d’Intelligence Artificielle, 37 (6), 1617–1627. https://doi.org/10.18280/ria.370626
  16. Vandana, Laxmi, V. (2023). The Detection and Classification of Diabetic Retinopathy using the Architectures of Deep Learning. International Journal For Multidisciplinary Research, 5 (6). https://doi.org/10.36948/ijfmr.2023.v05i06.10837
  17. Sanamdikar, S. T., Patil, S. A., Patil, D. O., Borawake, M. P. (2023). Enhanced Detection of Diabetic Retinopathy Using Ensemble Machine Learning: A Comparative Study. Ingénierie Des Systèmes d Information, 28 (6), 1663–1668. https://doi.org/10.18280/isi.280624
  18. Dai, L., Sheng, B., Chen, T., Wu, Q., Liu, R., Cai, C. et al. (2024). A deep learning system for predicting time to progression of diabetic retinopathy. Nature Medicine, 30 (2), 584–594. https://doi.org/10.1038/s41591-023-02702-z
  19. Zago, G. T., Andreão, R. V., Dorizzi, B., Teatini Salles, E. O. (2020). Diabetic retinopathy detection using red lesion localization and convolutional neural networks. Computers in Biology and Medicine, 116, 103537. https://doi.org/10.1016/j.compbiomed.2019.103537
  20. Qummar, S., Khan, F. G., Shah, S., Khan, A., Shamshirband, S., Rehman, Z. U. et al. (2019). A Deep Learning Ensemble Approach for Diabetic Retinopathy Detection. IEEE Access, 7, 150530–150539. https://doi.org/10.1109/access.2019.2947484
  21. Bakator, M., Radosav, D. (2018). Deep Learning and Medical Diagnosis: A Review of Literature. Multimodal Technologies and Interaction, 2 (3), 47. https://doi.org/10.3390/mti2030047
Increasing the reliability of diagnosis of diabetic retinopathy based on machine learning

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Published

2024-04-30

How to Cite

Mamyrbayev, O., Pavlov, S., Karas, O., Saldan, Y., Momynzhanova, K., & Zhumagulova, S. (2024). Increasing the reliability of diagnosis of diabetic retinopathy based on machine learning. Eastern-European Journal of Enterprise Technologies, 2(9 (128), 17–26. https://doi.org/10.15587/1729-4061.2024.297849

Issue

Vol. 2 No. 9 (128) (2024): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2024 Orken Mamyrbayev, Sergii Pavlov, Oleksandr Karas, Yosip Saldan, Kymbat Momynzhanova, Sholpan Zhumagulova

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