PriMera Scientific Engineering (ISSN: 2834-2550)

Review Article

Volume 5 Issue 2

Review Paper on Glaucoma Detection Using Machine Learning

Rohan Mathur*, Kushal Jha, Naman Gokharu and Sachin Bhandari

July 25, 2024

Abstract

Glaucoma is one of the leading causes of vision loss worldwide. Glaucoma cannot be cured in its advance stages. So, early detection of disease has become an important factor in the medical field. Numerous studies quickly became clear that using different image processing methods, the retinal fundus picture could be uncovered. In this study, many automated glaucoma detection techniques were thoroughly reviewed various papers were compared on the basis of the methodologies they adopted for detecting glaucoma from 2D fundus images created using CDR. 85% of glaucoma cases can be accurately detected by the majority of machine learning algorithms. First, image segmentation techniques like Elliptical Hough transform and edge detection gave the region of interest, i.e. optic disc and cup. These extracted images were then given to the machine learning and deep learning models to detect presence of glaucoma in the fundus image of the eye. The most significant deep learning, machine learning, and transfer learning methods for analyzing retinal images were reviewed, along with their benefits and drawbacks.

Keywords: Glaucoma detection; machine learning; deep learning; segmentation; neural network; Image processing; Optic Disc detection; Optic disc; Optic cup; Disc Ratio of optic cup (CDR); Fundus image

References

  1. MS Eswari and S Balamurali. “An Intelligent Machine Learning Support System for Glaucoma Prediction Among Diabetic Patients”. 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (2021): 447-449.
  2. X Chen., et al. “Glaucoma detection based on deep convolutional neural network”. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2015): 715-718.
  3. D Mahapatra and JM Buhmann. “A field of experts model for optic cup and disc segmentation from retinal fundus images”. 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) (2015): 218-221.
  4. Divya L and Jacob J. “Performance analysis of glaucoma detection approaches from fundus images”. Procedia computer science 143 (2018): 544-51.
  5. A Saxena., et al. “A Glaucoma Detection using Convolutional Neural Network”. 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC) (2020): 815-820.
  6. J Carrillo., et al. “Glaucoma Detection Using Fundus Images of The Eye”. 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA) (2019): 1-4.
  7. An G., et al. “Glaucoma diagnosis with machine learning based on optical coherence tomography and color fundus images”. Journal of healthcare engineering (2019).
  8. B Al-Bander., et al. “Automated glaucoma diagnosis using deep learning approach”. 2017 14th International Multi-Conference on Systems, Signals & Devices (SSD) (2017): 207-210.
  9. J Civit-Masot., et al. “Dual Machine-Learning System to Aid Glaucoma Diagnosis Using Disc and Cup Feature Extraction”. in IEEE Access 8 (2020): 127519-127529.
  10. Sarkar D and Das S. “Automated glaucoma detection of medical image using biogeography based optimization”. InAdvances in Optical Science and Engineering (2017): 381-388.
  11. Rao PV, Gayathri R and Sunitha R. “A novel approach for design and analysis of diabetic retinopathy glaucoma detection using cup to disk ration and ANN”. Procedia Materials Science 10 (2015): 446-54.
  12. Abbas Q. “Glaucoma-deep: detection of glaucoma eye disease on retinal fundus images using deep learning”. International Journal of Advanced Computer Science and Applications 8.6 (2017).
  13. Li L., et al. “Attention based glaucoma detection: a large-scale database and CNN model”. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (2019): 10571-10580.
  14. Thangaraj V and Natarajan V. “Glaucoma diagnosis using support vector machine”. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) (2017): 394-399.
  15. CE Willoughby., et al. “Anatomy and physiology of the human eye: effects of mucopolysaccharidoses disease on structure and function-a review”. Clin. Exp. Ophthalmol 38 (2010): 2-11.
  16. A Diaz-Pinto., et al. “Cnns for automatic glaucoma assessment using fundus images: An extensive validation”. Biomed. Eng. Online 18 (2019): 29.
  17. Bhandari S, Pathak S and Jain SA. “A Literature Review of Early- Stage Diabetic Retinopathy Detection Using Deep Learning and Evolutionary Computing Techniques”. Archives of Computational Methods in Engineering 13 (2022): 1-2.
  18. Bhandari S., et al. “A Review on Swarm intelligence & Evolutionary Algorithms based Approaches for Diabetic Retinopathy Detection”. In2022 IEEE World Conference on Applied Intelligence and Computing (AIC) (2022): 161-166.
  19. Laganowski HC, Muir MG and Hitchings RA. “Glaucoma and the iridocorneal endothelial syndrome”. Archives of Ophthalmology 110.3 (1992): 346-50.
  20. Ho CL and Walton DS. “Primary congenital glaucoma: 2004 update”. Journal of Pediatric Ophthalmology & Strabismus 41.5 (2004): 271-88.
  21. Erdurmuş M., et al. “Antioxidant status and oxidative stress in primary open angle glaucoma and pseudoexfoliative glaucoma”. Current eye research 36.8 (2011): 713-8.
  22. Hayreh SS. “Neovascular glaucoma”. Progress in retinal and eye research 26.5 (2007): 470-85.
  23. Bhandari S, Rambola R and Kumari R. “Swarm Intelligence and Evolutionary Algorithms for Diabetic Retinopathy Detection”. In Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development. Chapman and Hall/CRC (2019): 65-92.
  24. Aung T., et al. “A major marker for normal tension glaucoma: association with polymorphisms in the OPA1 gene”. Human genetics 110.1 (2002): 52-6.
  25. Törnquist R. “Chamber depth in primary acute glaucoma”. The British Journal of Ophthalmology 40.7 (1956): 421.
  26. Sugar HS and Barbour FA. “Pigmentary glaucoma: a rare clinical entity”. American Journal of Ophthalmology 32.1 (1949): 90-2.
  27. Ritch R, Schlötzer-Schrehardt U and Konstas AG. “Why is glaucoma associated with exfoliation syndrome?”. Progress in retinal and eye research 22.3 (2003): 253-75.
  28. Shabbir A., et al. “Detection of glaucoma using retinal fundus images: A comprehensive review”. Mathematical Biosciences and Engineering 18.3 (2021): 2033-76.
  29. Milder E and Davis K. “Ocular trauma and glaucoma”. International ophthalmology clinics 48.4 (2008): 47-64.
  30. Papadaki TG., et al. “Long-term results of Ahmed glaucoma valve implantation for uveitic glaucoma”. Am J Ophthalmol 144.1 (2007): 62-69.
  31. SM Shankaranarayana., et al. “Joint optic disc and cup segmentation using fully convolutional and adversarial networks”. in OMIA 2017, Fetal, Infant and Ophthalmic Medical Image Analysis): 168-176, Springer International Publishing (2017).
  32. L Quaranta., et al. “Quality of life in glaucoma: a review of the literature”. Advances in therapy 33.6 (2016): 959-981.
  33. A Septiarini., et al. “Optic disc and cup segmentation by automatic thresholding with morphological operation for glaucoma evaluation”. Signal, Image and Video Processing 11.5 (2017): 945-952.
  34. J Civit-Masot., et al. “Tpu cloud-based generalized u-net for eye fundus image segmentation”. IEEE Access 7 (2016): 142379-1.
  35. A Diaz-Pinto., et al. “Cnns for automatic glaucoma assessment using fundus images: an extensive validation”. Biomedical engineering online 18.1 (2019): 29.
  36. J Civit-Masot., et al. “Multidataset incremental training for optic disc segmentation”. in Proceedings of the 21st EANN (Engineering Applications of Neural Networks), Springer-Nature (2020).
  37. A Sevastopolsky. “Optic disc and cup segmentation methods for glaucoma detection with modification of u-net convolutional neural network”. Pattern Recognition and Image Analysis 27.3 (2017): 618-624.
  38. J Zilly, JM Buhmann and D Mahapatra. “Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation”. Computerized Medical Imaging and Graphics 55 (2017): 28-41.
  39. B Al-Bander., et al. “Dense fully convolutional segmentation of the optic disc and cup in colour fundus for glaucoma diagnosis”. Symmetry 10.4 (2018): 87.
  40. R Asaoka., et al. “Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier”. Ophthalmology 123.9 (2016): 1974-1980.
  41. Saha R, Chowdhury AR and Banerjee S. “Diabetic retinopathy related lesions detection and classification using machine learning technology”. In International Conference on Artificial Intelligence and Soft Computing (2016): 734-745.
  42. Dasgupta M and Banerjee S. “Case Based Reasoning in the Detection of Abnormalities in Retina Images: A Survey”. International Journal of Research in Electronics and Computer Engineering 2.2 (2014): 93-9.
  43. Kim SJ, Cho KJ and Oh S. “Development of machine learning models for diagnosis of glaucoma”. PloS one 12.5 (2017): e0177726.
  44. Oh S., et al. “Explainable machine learning model for glaucoma diagnosis and its interpretation”. Diagnostics 11.3 (2021): 510.
  45. Lamba K and Ahuja S. “State-of-Art Review on Automated Glaucoma Detection Using Machine Learning Techniques”. ECS Transactions 107.1 (2022): 9719.
  46. Wu CW., et al. “Comparison of Different Machine Learning Classifiers for Glaucoma Diagnosis Based on Spectralis OCT”. Diagnostics 11.9 (2021): 1718.
  47. Stalin David D and Jayachandran A. “A new expert system based on hybrid colour and structure descriptor and machine learning algorithms for early glaucoma diagnosis”. Multimedia Tools and Applications 79.7 (2020): 5213-24.
  48. Maetschke S., et al. “A feature agnostic approach for glaucoma detection in OCT volumes”. PloS one 14.7 (2019): e0219126.
  49. Mehta P., et al. “Automated detection of glaucoma with interpretable machine learning using clinical data and multimodal retinal images”. American Journal of Ophthalmology 231 (2021): 154-69.
  50. Thakur N and Juneja M. “Classification of glaucoma using hybrid features with machine learning approaches”. Biomedical Signal Processing and Control 62 (2020): 102137.