Implementation of convolutional neural network for predicting glaucoma from fundus images

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.269229

Keywords:

deep learning, glaucoma, convolutional neural network, fundus image, image processing

Abstract

The contributions of this paper are two-fold. First, it uses machine learning tools to detect and monitor glaucoma. Second, it provides insight into medicine, which can assist healthcare professionals in improving disease diagnostic accuracy and help to reduce the progression and degeneration of retinal ganglion cells in patients. Glaucoma is a group of eye diseases that damage the optic nerve, which can cause vision loss and blindness at any age. The main symptom of glaucoma in the early stages is high internal eye pressure. It is still unknown what causes these diseases to develop, but if not treated, they result in optic nerve atrophy. For this reason, in this paper we propose a novel deep learning system for the automatic diagnosis of glaucoma using a convolutional neural network for classification, which demonstrates improved performance and records computation time for fundus images. The results showed an accuracy of 94 % and a loss value of only 0.27. The model we have created to investigate with Keras helped us achieve good results in our training and testing process. These study results demonstrate the ability of a deep learning model to identify glaucoma from fundus images. Increasing the filter size and training the model resulted in a higher accuracy rate. A population survey that was conducted in 2019 shows that most patients with glaucoma become aware of their disease late, after the disease causes a high level of optic nerve damage and a high percentage of vision loss. Early diagnosis and detection of glaucoma using optic nerve imaging technology have gained wide clinical interest in stopping or slowing the progression of the disease, allowing the development of new algorithms to automate the diagnosis of eye diseases

Supporting Agency

  • This research has been funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant No. AP13068032).

Author Biographies

Sabina Rakhmetulayeva, International Information Technology University

PhD, Associate Professor

Department of Information Systems

Zarina Syrymbet, International Information Technology University

Bachelor of Science in Computer Science, Master Student

Department of Information Systems

References

  1. Myltykbay, N. U. (2021). Otsenka effektivnosti skrininga glaukomy v Respublike Kazahstan, Molodoy ucheniy, 53 (395).
  2. Botabekova, T. K., Aldasheva, N. A., Tashtitova, L. B., Islamova, S. E., Asylbekova, A. A. (2010), Effektivnost' gosudarstvennogo skrininga na glaukomu v Respublike Kazahstan. Tochka zreniya. Vostok - Zapad, 1, 9–11. Available at: https://eyepress.ru/article.aspx?20618
  3. Botabekova, T. K., Aldasheva, N. A., Islamova, S. E., Kramorenko, J. S. (2010). Efficacy of emergence of glaucoma based on different type of screening. Oftal'mologicheskie vedomosti, 3 (4), 29–31.
  4. Duisebekova, K. S., Kozhamzharova, D. K., Rakhmetulayeva, S. B., Umarov, F. A., Aitimov, M. Zh. (2020). Development of an information-analytical system for the analysis and monitoring of climatic and ecological changes in the environment. Procedia Computer Science, 170, 578–583. doi: https://doi.org/10.1016/j.procs.2020.03.128
  5. Sabina, R., Zarina, S. (2022). Convolutional Neural Network Analysis of Fundus for Glaucoma Diagnosis. 2022 International Conference on Smart Information Systems and Technologies (SIST). doi: https://doi.org/10.1109/sist54437.2022.9945723
  6. Bhatkalkar, B. J., Reddy, D. R., Prabhu, S., Bhandary, S. V. (2020). Improving the Performance of Convolutional Neural Network for the Segmentation of Optic Disc in Fundus Images Using Attention Gates and Conditional Random Fields. IEEE Access, 8, 29299–29310. doi: https://doi.org/10.1109/access.2020.2972318
  7. Civit-Masot, J., Dominguez-Morales, M. J., Vicente-Diaz, S., Civit, A. (2020). Dual Machine-Learning System to Aid Glaucoma Diagnosis Using Disc and Cup Feature Extraction. IEEE Access, 8, 127519–127529. doi: https://doi.org/10.1109/access.2020.3008539
  8. Civit-Masot, J., Luna-Perejon, F., Vicente-Diaz, S., Rodriguez Corral, J. M., Civit, A. (2019). TPU Cloud-Based Generalized U-Net for Eye Fundus Image Segmentation. IEEE Access, 7, 142379–142387. doi: https://doi.org/10.1109/access.2019.2944692
  9. Ting, D. S. W., Pasquale, L. R., Peng, L., Campbell, J. P., Lee, A. Y., Raman, R. et al. (2018). Artificial intelligence and deep learning in ophthalmology. British Journal of Ophthalmology, 103 (2), 167–175. doi: https://doi.org/10.1136/bjophthalmol-2018-313173
  10. Akkara, J., Ajitha, S., Judy, M. (2021). Identification of glaucoma from fundus images using deep learning techniques. Indian Journal of Ophthalmology, 69 (10), 2702. doi: https://doi.org/10.4103/ijo.ijo_92_21
  11. Hemelings, R., Elen, B., Barbosa-Breda, J., Blaschko, M. B., De Boever, P., Stalmans, I. (2021). Deep learning on fundus images detects glaucoma beyond the optic disc. Scientific Reports, 11 (1). doi: https://doi.org/10.1038/s41598-021-99605-1
  12. Lee, T., Jammal, A. A., Mariottoni, E. B., Medeiros, F. A. (2021). Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs. American Journal of Ophthalmology, 225, 86–94. doi: https://doi.org/10.1016/j.ajo.2020.12.031
  13. Devecioglu, O. C., Malik, J., Ince, T., Kiranyaz, S., Atalay, E., Gabbouj, M. (2021). Real-Time Glaucoma Detection From Digital Fundus Images Using Self-ONNs. IEEE Access, 9, 140031–140041. doi: https://doi.org/10.1109/access.2021.3118102
  14. Zhou, C., Ye, J., Wang, J., Zhou, Z., Wang, L., Jin, K. et al. (2022). Improving the generalization of glaucoma detection on fundus images via feature alignment between augmented views. Biomedical Optics Express, 13 (4), 2018. doi: https://doi.org/10.1364/boe.450543
  15. Song, W. T., Lai, I.-C., Su, Y.-Z. (2021). A Statistical Robust Glaucoma Detection Framework Combining Retinex, CNN, and DOE Using Fundus Images. IEEE Access, 9, 103772–103783. doi: https://doi.org/10.1109/access.2021.3098032
  16. Diaz-Pinto, A., Morales, S., Naranjo, V., Köhler, T., Mossi, J. M., Navea, A. (2019). CNNs for automatic glaucoma assessment using fundus images: an extensive validation. BioMedical Engineering OnLine, 18 (1). doi: https://doi.org/10.1186/s12938-019-0649-y
  17. Rakhmetulayeva, S. B., Duisebekova, K. S., Kozhamzharova, D. K., Aitimov, M. Zh. (2021). Pollutant transport modeling using Gaussian approximation for the solution of the semi-empirical equation. Journal of Theoretical and Applied Information Technology, 99 (8), 1730–1739. Available at: http://www.jatit.org/volumes/Vol99No8/3Vol99No8.pdf
  18. Carrillo, J., Bautista, L., Villamizar, J., Rueda, J., Sanchez, M., rueda, D. (2019). Glaucoma Detection Using Fundus Images of The Eye. 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA). doi: https://doi.org/10.1109/stsiva.2019.8730250
  19. Shabbir, A., Rasheed, A., Shehraz, H., Saleem, A., Zafar, B., Sajid, M. et al. (2021). Detection of glaucoma using retinal fundus images: A comprehensive review. Mathematical Biosciences and Engineering, 18 (3), 2033–2076. doi: https://doi.org/10.3934/mbe.2021106
  20. Abdullah, F., Imtiaz, R., Madni, H. A., Khan, H. A., Khan, T. M., Khan, M. A. U., Naqvi, S. S. (2021). A Review on Glaucoma Disease Detection Using Computerized Techniques. IEEE Access, 9, 37311–37333. doi: https://doi.org/10.1109/access.2021.3061451
  21. Islam, M., Poly, T. N., Yang, H. C., Atique, S., Li, Y. J. (2020). Deep Learning for Accurate Diagnosis of Glaucomatous Optic Neuropathy Using Digital Fundus Image. A Meta-Analysis. Studies in Health Technology and Informatics, 270, 153–157. doi: https://doi.org/10.3233/SHTI200141
  22. Alghamdi, M., Abdel-Mottaleb, M. (2021). A Comparative Study of Deep Learning Models for Diagnosing Glaucoma From Fundus Images. IEEE Access, 9, 23894–23906. doi: https://doi.org/10.1109/access.2021.3056641
  23. Guo, J., Azzopardi, G., Shi, C., Jansonius, N. M., Petkov, N. (2019). Automatic Determination of Vertical Cup-to-Disc Ratio in Retinal Fundus Images for Glaucoma Screening. IEEE Access, 7, 8527–8541. doi: https://doi.org/10.1109/access.2018.2890544
  24. Large-scale Database with CNN Model. Available at: https://github.com/smilell/AG-CNN
  25. Hasan, M. K., Tanha, T., Amin, M. R., Faruk, O., Khan, M. M., Aljahdali, S., Masud, M. (2021). Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods. Computational and Mathematical Methods in Medicine, 2021, 1–11. doi: https://doi.org/10.1155/2021/7666365
  26. Wu, J.-H., Nishida, T., Weinreb, R. N., Lin, J.-W. (2022). Performances of Machine Learning in Detecting Glaucoma Using Fundus and Retinal Optical Coherence Tomography Images: A Meta-Analysis. American Journal of Ophthalmology, 237, 1–12. doi: https://doi.org/10.1016/j.ajo.2021.12.008
  27. Buisson, M., Navel, V., Labbé, A., Watson, S. L., Baker, J. S., Murtagh, P. et al. (2021). Deep learning versus ophthalmologists for screening for glaucoma on fundus examination: A systematic review and meta‐analysis. Clinical & Experimental Ophthalmology, 49 (9), 1027–1038. doi: https://doi.org/10.1111/ceo.14000
  28. Classifying CIFAR100 images using ResNets, Regularization and Data Augmentation in PyTorch. Available at: https://jovian.ai/tessdja/resnet-practice-cifar100-resnet
  29. Mariottoni, E. B., Jammal, A. A., Berchuck, S. I., Shigueoka, L. S., Tavares, I. M., Medeiros, F. A. (2021). An objective structural and functional reference standard in glaucoma. Scientific Reports, 11 (1). doi: https://doi.org/10.1038/s41598-021-80993-3
  30. Rakhmetulayeva, S. B., Duisebekova, K. S., Mamyrbekov, A. M., Kozhamzharova, D. K., Astaubayeva, G. N., Stamkulova, K. (2018). Application of Classification Algorithm Based on SVM for Determining the Effectiveness of Treatment of Tuberculosis. Procedia Computer Science, 130, 231–238. doi: https://doi.org/10.1016/j.procs.2018.04.034
  31. Sagar, M. A. K., Dai, B., Chacko, J. V., Weber, J. J., Velten, A., Sanders, S. T. et al. (2019). Optical fiber-based dispersion for spectral discrimination in fluorescence lifetime imaging systems. Journal of Biomedical Optics, 25 (01), 1. doi: https://doi.org/10.1117/1.jbo.25.1.014506

Downloads

Published

2022-12-30

How to Cite

Rakhmetulayeva, S., & Syrymbet, Z. (2022). Implementation of convolutional neural network for predicting glaucoma from fundus images. Eastern-European Journal of Enterprise Technologies, 6(2 (120), 70–77. https://doi.org/10.15587/1729-4061.2022.269229

Issue

Vol. 6 No. 2 (120) (2022): Information technology. Industry control systems

Section

Information technology

License

Copyright (c) 2022 Sabina Rakhmetulayeva, Zarina Syrymbet

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.