Optical Cup and Disc Segmentation using Deep Learning Technique for Glaucoma Detection

##plugins.themes.academic_pro.article.sidebar##

Published Feb 15, 2023
https://doi.org/10.47164/ijngc.v14i1.1017
Download
Requires Subscription pdf
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 14, Special Issue 1, February 2023

##plugins.themes.academic_pro.article.main##

Priya Parkhi
Shri Ramdeobaba College of Engineering and Management, Nagpur
Bhagyashree Hambarde Hambarde
Shri Randeobaba College of Engineering and Management, Nagpur

Abstract

The optic nerve damaging condition called Glaucoma. This disease is increment at an alarming rate. By the end of the 2044 there is possibility that across 111.8 million populations will be influenced by glaucoma. It is a neurodegenerative disease. If intravascular pressure is increases, optic nerve of the eye gets damage. This damage may cause permanent or total blindness in person. The Glaucoma is examined by an experienced ophthalmologist on the retinal part of the eye. This process required excessive equipment, experienced medical practitioners and also it take more time to work out manually. After considering this problem there is an extreme requirement of developing an automatic system which will effectively and automatically work properly in lack of any professional doctor and it should also take less time. Lots of different parameters are available to detect glaucoma but the
best parameter is to find out optical cup-to-disc-ratio. To increase or to enhance the precision and accuracy of the result, cup to disc value is needed to find CDR value. In order to detect glaucoma, automatic separation of the OC and DC is very essential to avoid any error. We use deeplabv3 architecture to perform segmentation of optic disc and cup and classification is done using ensemble machine learning. This proposes research achieve intersection over union (IOU) scores, 0.9423 for optic disc and 0.9310 for optic cup. We perform testing on globally accessible data-sets i.e. DRISHTI, ORIGA, and RIMONE with accuracy of 93%, 91% and 92% respectively

##plugins.themes.academic_pro.article.details##

Creative Commons License

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

How to Cite
Parkhi, P., & Hambarde, B. H. (2023). Optical Cup and Disc Segmentation using Deep Learning Technique for Glaucoma Detection. International Journal of Next-Generation Computing, 14(1). https://doi.org/10.47164/ijngc.v14i1.1017
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. A. Sarhan, J. R. and Alhaj, R. Glaucoma detection using image processing , journal =Computerized Medical Imaging and Graphics, year = 2019
  2. A. Sarhaqqn, J. R. and Alhajj, R. 2019. Glaucoma detection using image processing techniques: a literature review. Computerized Medical Imaging and Graphics:Official Journal of the Computerized Medical Imaging Society. DOI: https://doi.org/10.1016/j.compmedimag.2019.101657
  3. Akshat Tulsani, Preetham Kumar, S. P. 2021. Automated segmentation of optic disc and optic cup for glaucoma assessment using improved unet++ architecture. Medical Imaging:Image Processing. DOI: https://doi.org/10.1016/j.bbe.2021.05.011
  4. Diaz-Pinto, A., M. S. N. V. K. T. M. J. and Navea, A. 2019. Cnns for automatic glaucoma assessment using fundus images: an extensive validation. BioMed Eng Online 18 . DOI: https://doi.org/10.1186/s12938-019-0649-y
  5. Fatima Bokhari ST, Sharif M, Y. M. F. S. 2017. Fundus image segmentation and feature extraction for the detection of glaucoma: A new approach. Cur Med Imaging Vol.4, pp.4327–4354.
  6. Fu H, Cheng J, X. Y. W. D. L. J. C. X. J. 2018. optic disc and cup segmentation based on multi-label deep network. . IEEE Tran med imaging. DOI: https://doi.org/10.1109/TMI.2018.2791488
  7. Guo F, Li W, T. J. e. a. 2020. Automated glaucoma screening method based on image segmentation and feature extraction. Med Biol Eng Compu Vol.4, pp.4327–4354.
  8. Haleem MS, Han L, H. J. e. a. 2016. Regional image features model for automatic classification between normal and glaucoma in fundus and scanning laser ophthalmoscopy (slo) images. J Med Systems DOI: https://doi.org/10.1007/s10916-016-0482-9
  9. H.N. Veena, A. Muruganandham b, T. S. K. 3 February 2021. A novel optic disc and optic cup segmentation technique to diagnose glaucoma using deep learning convolutional neural
  10. network over retinal fundus images. in Journal of King Saud University – Computer and
  11. Information Sciences.
  12. Issac A, Sarathi MP, D. M. e. a. 2015. An adaptive threshold based image processing technique for
  13. improved glaucoma detection and classification. Comput Methods Programs
  14. Biomed Vol.4, pp.4327–4354.
  15. L. Gagnon, M. Lalonde, M. B. and Boucher, M.-C. 2001. Procedure to detect anatomical structures in optical fundus images. Medical Imaging: Image Processing Vol.4322, pp.1218–1225. DOI: https://doi.org/10.1117/12.430999
  16. Li, H. and Chutatape, O. 2001. Automatic location of optic disk in retinal images. the IEEE
  17. International Conference on Image Processing (ICIP ’01).
  18. Li L, Xu M, L. H. L. Y. W. X. J. L. e. a. 2020. A large-scale database and a cnn model for attention-based glaucoma detection. IEEE Trans Med Imaging Vol.4, pp.4327–4354.
  19. Lotankar M, Noronha K, K. J. 2015. Detection of optic disc and cup from color retinal images for automated diagnosis of glaucoma. IEEE UP section conference on electrical computer and electronics (UPCON). DOI: https://doi.org/10.1109/UPCON.2015.7456741
  20. Lotankar M, Noronha K, K. J. e. a. 2021. Detection of optic disc and cup from color retinal images for automated diagnosis of glaucoma. : Proceedings of IEEE UP Section Conference on Electrical Computer and Electronics.
  21. M, G. 2019. Semi-supervised transfer learning for convolutional neural networks for glaucoma detection. IEEE International Conference on Acoustics, Speech and Signal Processing Vol.4,
  22. pp.4327–4354.
  23. M. C. V. Stella Mary, E. B. R. and Naik, G. R. 2016. Retinal fundus image analysis for diagnosis of Glaucoma: a comprehensive survey. IEEE Access Vol.4, pp.4327–4354. DOI: https://doi.org/10.1109/ACCESS.2016.2596761
  24. Nisha Wankhade, K. B. 2021. Multi-class retinopathy classification in fundus image using deep learning approaches. International journal of Next-Generation Computing. DOI: https://doi.org/10.47164/ijngc.v12i5.454
  25. Nugroho HA, Oktoeberza KZW, A. T. N. F. 2015. Detection of exudates on color fundus images using texture based feature extraction. Int J Technol. DOI: https://doi.org/10.14716/ijtech.v6i2.958
  26. Perdomo O, Andrearczyk V, M. F. M. H. G. a. F. 2017. Glaucoma diagnosis from eye fundus images based on deep morphometric feature estimation.”. Computational Pathology and Ophthalmic Medical Image Analysis. DOI: https://doi.org/10.1007/978-3-030-00949-6_38
  27. Shuang Yu, Di Xiao, S. F. Y. K. 2019. Robust optic disc and cup segmentation with deep learning for glaucoma detection. Computerized Medical Imaging and Graphics.
  28. Trupti Vasantrao Bhandare, S. R. April 2021. Review on heart disease diagnosis using deep learning methods. International journal of Next-Generation Computing.
  29. Weinreb RN, Aung T, M. F. 2014. The pathophysiology and treatment of glaucoma: a review. JAMA Vol.63, No.4 DOI: https://doi.org/10.1001/jama.2014.3192