Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review

Main Article Content

Jincy C. Mathew
V. Ilango
V. Asha


Globally, glaucoma is the most common factor in both permanent blindness and impairment. However, the majority of patients are unaware they have the condition, and clinical practise continues to face difficulties in detecting glaucoma progression using current technology. An expert ophthalmologist examines the retinal portion of the eye to see how the glaucoma is progressing. This method is quite time-consuming, and doing it manually takes more time. Therefore, using deep learning and machine learning techniques, this problem can be resolved by automatically diagnosing glaucoma. This systematic review involved a comprehensive analysis of various automated glaucoma prediction and detection techniques. More than 100 articles on Machine learning (ML) techniques with understandable graph and tabular column are reviewed considering summery, method, objective, performance, advantages and disadvantages. In the ML techniques such as support vector machine (SVM), and K-means. Fuzzy c-means clustering algorithm are widely used in glaucoma detection and prediction. Through the systematic review, the most accurate technique to detect and predict glaucoma can be determined which can be utilized for future betterment.

Article Details

How to Cite
Mathew, J. C. ., Ilango, V. ., & Asha, V. . (2023). Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), 283–309.
Review Paper


J. L. Wiggs, & L. R. Pasquale, "Genetics of glaucoma," Human molecular genetics vol. 26, no. R1, pp. R21-R27, 2017.

R. Lim, & I. Goldberg, The Glaucoma Book: A Practical, Evidence-Based Approach to Patient Care, (Chapter 1, p. 3) (2010).

R. J. Casson, G. Chidlow, J. P. Wood, J. G. Crowston, & I. Goldberg, "Definition of glaucoma: clinical and experimental concepts," Clinical & experimental ophthalmology vol. 40, no. 4, pp. 341-349, 2012.

R. R. Bourne, "The optic nerve head in glaucoma," Community Eye Health vol. 19, no. 59, pp. 44, 2006.

C. W. McMonnies, "Glaucoma history and risk factors," Journal of optometry vol. 10, no. 2, pp. 71-78, 2017.

R. J. Casson, G. Chidlow, J. P. Wood, J. G. Crowston, & I. Goldberg, Defnition of glaucoma: Clinical and experimentalconcepts. Clin. Exp. Ophthalmol. Vol. 40, pp. 341–349, 2012.

D. R. Anderson, Collaborative normal tension glaucoma study. Curr Opin Ophthalmol. Vol. 14, pp. 86–90, 2003.

C. Bowd, R. N. Weinreb, J. M. Williams, & L. M. Zangwill, The retinal nerve fiber layer thickness in ocular hypertensive, normal, and glaucomatous eyes with optical coherence tomography. Arch Ophthalmol. Vol. 118, pp. 22–26, 2000.

R. R. Bourne, The optic nerve head in glaucoma, Commun. Eye Health. Vol. 19, no. 59, () pp. 44–45, 2006.

. D. L. Budenz, K. Barton, J. Whiteside-de Vos, J. Schiffman, J. Bandi, W. Nolan, & Tema Eye Survey Study Group. Prevalence of glaucoma in an urban West African population: the Tema Eye Survey. JAMA Ophthalmol. Vol. 131, pp. 651–658, 2013.

. A. Hennis, S. Y. Wu, B. Nemesure, R. Honkanen, M. C. Leske, & Barbados Eye Studies Group. Awareness of incident open-angle glaucoma in a population study: the Barbados Eye Studies. Ophthalmology. Vol. 114, pp. 1816–1821, 2007.

. R. S. Harwerth, L. Carter-Dawson, E. L. Smith, G. Barnes, W. F. Holt, & M. L. Crawford, Neural Losses Correlated with Visual Losses in Clinical Perimetry. Invest Ophthalmol Vis Sci. vol. 45, pp. 3152–3160, 2004.

. M. Ulieru, O. Cuzzani, S. H. Rubin, & M. G. Ceruti, "Application of soft computing methods to the diagnosis and prediction of glaucoma." In Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics.'cybernetics evolving to systems, humans, organizations, and their complex interactions'(cat. no. 0, vol. 5, pp. 3641-3645, 2000. IEEE.

R. Asaoka, H. Murata, K. Hirasawa, Y. Fujino, M. Matsuura, A. Miki, & M. Araie, Using deep learning and transform learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images. Am J Ophthalmol. Vol. 198, pp. 136–145, 2019.

J. E. Morgan, N. J. L. Sheen, R. V. North, Y. Choong, & E. Ansari, “Digital imaging of the optic nerve head: monoscopic and stereoscopic analysis,” British Journal of Ophthalmology, vol. 89, no. 7, pp. 879–884, 2005.

E. Balan, C. Venkatesan, M. G. Sumithra, M. Akila, & M. Manikandan, "Method for detecting cup to disk ratio for the prediction of glaucoma disease–A review," 2020.

P. Harasymowycz, C. Birt, P. Gooi, L. Heckler, C. Hutnik, D. Jinapriya, & R. Day, “Medical Management of Glaucoma in the 21st Century from a Canadian Perspective,” J Ophthalmol. pp. 6509809, 2016. 10.1155/2016/6509809

A. P. Rotchford, J. F. Kirwan, M. A. Muller, G. J. Johnson, & P. Roux, “Temba glaucoma study: a population-based cross-sectional survey in urban South Africa,” Ophthalmology. Vol. 110, pp. 376–82, 2003.

S. Thomas, W. Hodge, & M. Malvankar-Mehta, “The cost-effectiveness analysis of teleglaucoma screening device,” PLoS One. Vol. 10, pp. e0137913, 2015. 10.1371/journal.pone.0137913

C. Imrie, & A. J. Tatham, “Glaucoma: the patient's perspective,” Br J Gen Pract. Vol. 66, pp. e371-373, 2016. 10.3399/bjgp16X685165

H. Hashemi, M. Mohammadi, N. Zandvakil, M. Khabazkhoob, M. H. Emamian, M. Shariati, & A. Fotouhi, “Prevalence and risk factors of glaucoma in an adult population from Shahroud, Iran,” J Curr Ophthalmol. Vol. 31, pp. 366-372, 2018. 10.1016/j.joco.2018.05.003.

R. N. Weinreb, C. K. Leung, J. G. Crowston, F. A. Medeiros, D. S. Friedman, J. L. Wiggs, & K. R. Martin, “Te pathophysiology and treatment of glaucoma: a review,” JAMA vol. 311, pp. 1901–1911, 2014.

M. T. Leite, L. M. Sakata, & F. A. Medeiros, “Managing glaucoma in developing countries,” Arq. Bras. Ofalmol. Vol. 74, pp. 83–84, 2011.

A. Hennis, S. Y. Wu, B. Nemesure, R. Honkanen, M. C. Leske, et al. “Awareness of incident open-angle glaucoma in a population study: the Barbados Eye Studies,” Ophthalmology vol. 114, pp. 1816–1821, 2007.

R. Susanna, C. G. De Moraes, G. A. Cioffi, & R. Ritch, “Why do people (still) go blind from glaucoma?. Transl,” Vis.Sci. Technol. Vol. 4, no. 1, 2015.

P. Founti, A. L. Coleman, M. R. Wilson, F. Yu, E. Anastasopoulos, A. Harris, & F. Topouzis, Overdiagnosis of open-angle glaucoma in the general population: the Tessaloniki Eye Study. Acta Ophthalmol. Vol. 96, pp. e859–e864, 2018.

V. Grau, J. C. Downs, and C. F. Burgoyne, ‘‘Segmentation of trabeculated structures using an anisotropic Markov random field: Application to the study of the optic nerve head in glaucoma,’’ IEEE Trans. Med. Imag., vol. 25, no. 3, pp. 245–255, Mar. 2006.

H. A. Quigley and A. T. Broman, ‘‘The number of people with glaucoma worldwide in 2010 and 2020,’’ Brit. J. Ophthalmol., vol. 90, pp. 262–267, Mar. 2006.

F. Fumero, S. Alayon, J. L. Sanchez, J. Sigut, and M. Gonzalez-Hernandez, ‘‘RIM-ONE: An open retinal image database for optic nerve evaluation,’’ in Proc. 24th Int. Symp. Comput.-Based Med. Syst., pp. 1–6, 2011.

D. S. W. Ting, C. Y.-L. Cheung, G. Lim et al., “Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes,” JAMA, vol. 318, no. 22, pp. 2211–2223, 2017.

F. A. Medeiros, L. M. Zangwill, C. Bowd, and R. N. Weinreb, ‘‘Comparison of the GDx VCC scanning laser polarimeter, HRT II confocalscanning laser ophthalmoscope, and stratus oct optical coherence tomographfor the detection of glaucoma,’’ Arch. Opthalmol., vol. 122, pp. 827–837, Jun. 2004.

P. Naithani et al., ‘‘Evaluation of optical coherence tomography and Heidelberg retinal tomography parameters in detecting early and moderate glaucoma,’’ Invest. Ophthalmol. Vis. Sci., vol. 48, pp. 3138–3145, Jul. 2007.

L. M. Ventura, N. Sorokac, R. De Los Santos, W. J. Feuer, and V. Porciatti, ‘‘The relationship between retinal ganglion cell function and retinal nerve fiber thickness in early glaucoma,’’ Invest. Ophthalmol. Vis. Sci., vol. 47, pp. 3904–3911, Sep. 2006.

R. S. Harwerth and H. A. Quigley, ‘‘Visual field defects and retinal ganglion cell losses in patients with glaucoma,’’ Arch. Opthalmol., vol. 124, pp. 853–859, Jun. 2006.

J. De Fauw, J. R. Ledsam, B. Romera-Paredes et al., “Clinically applicable deep learning for diagnosis and referral in retinal disease,” Nature Medicine, vol. 24, no. 9, pp. 1342–1350, 2018.

E. J. Higginbotham, & D. A. Lee, Clinical guide to glaucoma management. Woburn, MA: Butterworth Heinemann, pp. 156-70, 2004.

J. S. Distelhorst, & G. M. Hughes, "Open-angle glaucoma." American family physician vol. 67, no. 9, pp. 1937-1944, 2003.

H. A. Quigley, S. K. West, J. Rodriguez, B. Munoz, R. Klein, & R. Snyder, “The prevalence of glaucoma in a population-based study of Hispanic subjects: Proyecto VER,” Arch Ophthalmol. Vol. 119, pp. 1819–26, 2001.

S. Y. Shen, T. Y. Wong, P. J. Foster, J.-L. Loo, M. Rosman, S.-C. Loon, W. L. Wong, S.-M. Saw, and T. Aung, “The prevalence and types of glaucoma in malay people: The singapore malay eye study,” Investigative Ophthalmology and Visual Science, vol. 49, no. 9, p. 3846, 2008.

Y. H. Kwon, J. H. Fingert, M. H. Kuehn, & W. L. Alward, "Primary open-angle glaucoma," New England Journal of Medicine vol. 360, no. 11, pp. 1113-1124, 2009.

R. Sihota, N. C. Lakshmaiah, H. C. Agarwal, R. M. Pandey, and J. S. Titiyal. "Ocular parameters in the subgroups of angle closure glaucoma." Clinical & experimental ophthalmology vol. 28, no. 4, pp. 253-258, 2000.

‘Types of Glaucoma’,, Access Date: 12 January 2016.

‘Diseases and Conditions Glaucoma’,, Access Date: 13 January 2016

P. Tarongoy, C. L. Ho, & D. S. Walton, "Angle-closure glaucoma: the role of the lens in the pathogenesis, prevention, and treatment," Survey of ophthalmology vol. 54, no. 2, pp. 211-225, 2009.

P. J. Foster, & G. J. Johnson, “Glaucoma in china: how big is the problem?” Br. J. Ophthalmol., vol. 85, no. 11, pp. 12771282, 2001.

S. Salim, “The role of anterior segment optical coherence tomography in glaucoma,” Journal of Ophthalmology, pp. 1–9, 2012.

M. Nongpiur, B. Haaland, D. Friedman, S. Perera, M. He, L. Foo, M. Baskaran, L. Sakata, T. Wong, and T. Aung, “Classification algorithms based on anterior segment optical coherence tomography measurements for detection of angle closure,” Ophthalmology, vol. 120, no. 1, pp. 48–54, 2013.

C. Leung and R. Weinreb, “Anterior chamber angle imaging with optical coherence tomography,” Eye, vol. 25, no. 3, pp. 261–267, 2011.

I. Lai, H. Mak, G. Lai, M. Yu, D. Lam, and C. Leung, “Anterior chamber angle imaging with swept-source optical coherence tomography: Measuring peripheral anterior synechia in glaucoma,” Ophthalmology, vol. 120, no. 6, pp. 1144–1149, 2013.

N. Wang, H. Wu, Z. Fan, “Primary angle closure glaucoma in Chinese and Western populations,” Chin Med J, vol. 115, pp. 1706-15, 2002.

M. E. Nongpiur, J. Y. Ku, & T. Aung, "Angle closure glaucoma: a mechanistic review," Current opinion in ophthalmology vol. 22, no. 2, pp. 96-101, 2011.

Y. Ikuno, K. Sayanagi, T. Oshima, F. Gomi, S. Kusaka, M. Kamei, M. Ohji, T. Fujikado, and Y. Tano, “Optical coherence tomographic findings of macular holes and retinal detachment after vitrectomy in highly myopic eyes,” American Journal of Ophthalmology, vol. 136, no. 3, pp. 477–481, 2003.

A. Bagci, M. Shahidi, R. Ansari, and M. Blair, “Thickness profiles of retinal layers by optical coherence tomography image segmentation,” American Journal of Ophthalmology, vol. 146, no. 5, pp. 679–687, 2008.

M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns, ` and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Transactions on Medical Imaging, vol. 28, no. 9, pp. 1436–1447, 2009.

K. Lee, M. Niemeijer, M. K. Garvin, Y. Kwon, M. Sonka, and M. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of ` the optic nerve head,” IEEE Transactions on Medical Imaging, vol. 29, no. 1, pp. 159–168, 2010.

H. Fu, D. Xu, S. Lin, D. W. K. Wong, and J. Liu, “Automatic Optic Disc Detection in OCT Slices via Low-Rank Reconstruction,” IEEE Transactions on Biomedical Engineering, vol. 62, no. 4, pp. 1151–1158, 2015.

M. C. Leske, A. Heijl, M. Hussein, B. Bengtsson, L. Hyman, E. Komaroff, & Early Manifest Glaucoma Trial Group. "Factors for glaucoma progression and the effect of treatment: the early manifest glaucoma trial." Archives of ophthalmology 121, no. 1, pp. 48-56, 2003.

U. R. Acharya, S. Bhat, J. E. Koh, S. V. Bhandary, H. Adeli, “A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images,” Computers in biology and medicine vol. 88, pp. 72–83, 2017.

K. P. Noronha, U. R. Acharya, K. P. Nayak, R. J. Martis, S. V. Bhandary, “Automated classification of glaucoma stages using higher order cumulant features,” Biomedical Signal Processing and Control, vol. 10, pp. 174–183, 2014.

M. S. Haleem, L. Han, J. van Hemert, B. Li, “Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review,” Computerized medical imaging and graphics vol. 37, no. 7, pp. 581– 596, 2013.

J. De Fauw, J. R. Ledsam, B. Romera-Paredes et al., “Clinically applicable deep learning for diagnosis and referral in retinal disease,” Nature Medicine, vol. 24, no. 9, pp. 1342–1350, 2018.

M. R. K. Mookiah, U. R. Acharya, H. Fujita et al., “Local configuration pattern features for age-related macular degeneration characterization and classification,” Computers in Biology and Medicine, vol. 63, pp. 208–218, 2015.

B. AI-Bander, W. AI-Nuaimy, M. A. AI-Taee, and Y. Zheng, “Automated glaucoma diagnosis using deep learning approach,” in Proceedings of 14th International MultiConference on Systems, Signals & Devices, Marrakesh, Morocco, March 2017.

R. Asaoka, K. Hirasawa, A. Iwase et al., “Validating the usefulness of the “random forests” classifier to diagnose early glaucoma with optical coherence tomography,” American Journal of Ophthalmology, vol. 174, pp. 95–103, 2017.

S. Maetschke, B. Antony, H. Ishikawa, G. Wollstein, J. S. Schuman, and R. Garvani, “A feature agnostic approach for glaucoma detection in OCT volumes,” 2018, http://arxiv. org/abs/1807.04855.

J. Ker, L. Wang, J. Rao, and T. Lim, ‘‘Deep learning applications in medical image analysis,’’ IEEE Access, vol. 6, pp. 9375–9389, 2018.

S. Yousefi, M.H. Goldbaum, M. Balasubramanian, T.P. Jung, R.N. Weinreb, F.A. Medeiros, C. Bowd, Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points. IEEE Trans Biomed Eng vol. 61, no. 4, pp. 1143–1154, 2013.

A. Sarhan, J. Rokne, & R. Alhajj, "Glaucoma detection using image processing techniques: A literature review," Computerized Medical Imaging and Graphics vol. 78, pp. 101657, 2019.

C. B. Anusorn, W. Kongprawechnon, T. Kondo, S. Sintuwong, & K. Tungpimolrut, "Image processing techniques for glaucoma detection using the cup-to-disc ratio," Science & Technology Asia pp. 22-34, 2013.

I. Qureshi, "Glaucoma detection in retinal images using image processing techniques: a survey," International Journal of Advanced Networking and Applications vol. 7, no. 2, pp. 2705, 2015.

M. Madhusudhan, N. Malay, S. R. Nirmala, & D. Samerendra, "Image processing techniques for glaucoma detection," In International conference on advances in computing and communications, pp. 365-373. Springer, Berlin, Heidelberg, 2011.

R. Bock, J. Meier, L. G. Nyúl, J. Hornegger, & G. Michelson, "Glaucoma risk index: automated glaucoma detection from color fundus images," Medical image analysis vol. 14, no. 3, pp. 471-481, 2010.

Y. Xu, L. Duan, S. Lin, X. Chen, D.W.K. Wong, T.Y. Wong, J. Liu, (2014) “Optic cup segmentation for glaucoma detection using low-rank superpixel representation,” In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp. 788–795.

I. N. Bankman, “Ed., Handbook of Medical Image Processing and Analysis,” Baltimore, MD, USA: The Johns Hopkins Univ. Press, pp. 1084–1098, 2000.

S. R. Nirmala, M. K. Nath, and S. Dandapat, ‘‘Retinal image analysis: A review,’’ Int. J. Comput. Commun. Technol., vol. 2, pp. 11–15, Jul. 2011.

R. C. Gonzalez, “Digital Image Processing,” PHI, New Delhi-110001: Prentice Hall of India, Second ed., 2006.

W. H, H. W, G. KG, and L. ML, “An effective approach to detect lesions in color retinal images,” pp. 1–6, August 2000. Conference on Computer Vision and Pattern Recognition, IEEE,

U. R. Acharya, E. Y. K. Ng, and J. S. Suri, Image modelling of human eye. Artech House, MA, USA, 2008a, April.

T. Walter, J. C. Klein, P. Massin, and A. Erginay, “A contribution of image processing to the diagnosis of diabetic retinopathy— detection of exudates in color fundus images of the human retina,” IEEE Trans. Med. Imaging. Vol. 21, pp. 1236–1243, 2002.

M. Petrou, and C. Petrou, Image Processing the Fundamentals. Singapore: Wiley, second edition ed., 2010.

J. Meier, R. Bock, G. Michelson, L.G. Ny´ul, J. Hornegger, Effects of preprocessing eye fundus images on appearance based glaucoma classification. In: Procceedings of International Conference on Computer Analysis of Images and Patterns. 2007. (accepted for publication).

M. Lester, D. Garway-Heath, H. Lemij, Optic Nerve Head and Retinal Nerve Fibre Analysis. European Glaucoma Society. 2005.

S. Sri Abirami and et all.,” Glaucoma Images Classification Using Fuzzy Min-Max Neural Network Based On Data-Core,” International Journal of Science and Modern Engineering (IJISME) ISSN: 2319-6386, Volume-1, Issue-7, June 2013.

R. McIntyre and et all. , “Toward Glaucoma Classification with Moment Methods,” Proceedings of the First Canadian Conference on Computer and Robot Vision, IEEE, 2004.

H. Yuji and et all., “Vertical Cup-to-disk ratio measurement for diagnosis of glaucoma fundus images,” Medical Imaging 2010: Computer-Aided Diagnosis, vol. 7624,SPEI,2010.

J. Hornegger, H. Niemann, R. Risack, “Appearance-based object recognition using optimal feature transforms,” Pattern Recogn, vol. 2, no. 33, pp. 209–224, 2000.

A. Jain, F. Farrokhnia, “Unsupervised texture segmentation using Gabor filters. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics,” pp. 14–19, 1990. IEEE Computer Society Press, Los Alamitos.

H. N. jung, and et all. ,“Detection of Glaucoma Progression by Assessment of Segmented Macular Thickness Data Obtained Using Spectral Domain Optical Coherence Tomography” Investigative Ophthalmology & Visual Science, Vol. 53, No. 7, June 2012.

H. Chih-Yin, and et all., “An atomatic fundus image analysis system for clinical diagnosis of glaucoma,” International Conference on Complex, Intelligent, and Software Intensive Systems, IEEE, 2011.

K. Nouri-Mahdavi, R. Zarei, & J. Caprioli, "Influence of visual field testing frequency on detection of glaucoma progression with trend analyses," Archives of ophthalmology vol. 129, no. 12, pp. 1521-1527, 2011.

R. Bock, J. Meier, L. G. Nyul, J. Hornegger, and G. Michelson, (2010) “Glaucoma risk index: Automated glaucoma detection from color fundus images,” Medical Image Analysis vol. 14, no. 3, pp. 471 – 481.

Y. Jin and et all., “Automated Optic Nerve Analysis for Diagnostic Support in Glaucoma,” 8th IEEE Symposium on Computer-Based Medical Systems, IEEE, 2005.

A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Transaction on Medical Imaging, vol. 19, pp. 203–210, March 2000.

L. Zhang, Q. Li, J. You, and D. Zhang, “A modified matched filter with double sided thresholding for screening proliferative diabetic retinopathy,” IEEE Transactions on Information Technology in Biomedicine, vol. 13, pp. 528–534, July 2009.

Y. Hatanaka, T. Nakagawa, Y. Hayashi, T. Hara, and H. Fujita, “Improvement of automated detection method of haemorrhages in fundus images,” (Vancouver, British Columbia, Canada), pp. 5429–5432, EMBS, IEEE, August 2008.

S. Nirmala, S. Dandapat, and P. Bora, “Wavelet weighted blood vessel distortion measure for retinal images,” Biomedical Signal Processing and Control, vol. 5, pp. 282–291, July 2010.

A. Dey, S.K. Bandyopadhyay, “Automated glaucoma detection using support vector machine classification method,” J Adv Med Med Res 1–12, 2016.

X. Chen, Y. Xu, S. Yan, D.W.K. Wong, T.Y. Wong, J. Liu, “Automatic feature learning for glaucoma detection based on deep learning,” In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp. 669–677, 2015.

H. Fu et al., “Segmentation and Quantification for Angle-Closure Glaucoma Assessment in Anterior Segment OCT,” in IEEE Transactions on Medical Imaging, vol. 36, no. 9, Sept. 2017, pp. 1930–1938.

Y.-C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, and C.-Y. Cheng, ‘‘Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis,’’ Ophthalmology, vol. 121, no. 11, pp. 2081–2090, 2014.

S. Maheshwari, V. Kanhangad, R. B. Pachori, S. V. Bhandary, U. R. Acharya, Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques, Comput. Biol. Med., vol. 105, pp. 72–80, 2019.

S. Masood, M. Sharif, M. Raza, M. Yasmin, M. Iqbal, M. Younus Javed, “Glaucoma disease: A survey,” Curr. Med. Imaging, vol. 11, pp. 272–283, 2015.

F. Cabitza, R. Rasoini, G.F. Gensini, “Unintended consequences of machine learning in medicine,” JAMA. Vol. 318, no. 6, pp. 517–8, 2017.

D. Komura, S. Ishikawa, “Machine learning methods for histopathological image analysis,” Comput Struct Biotechnol J. vol. 16, pp. 34–4, 2018.

Z. Burgansky-Eliash, G. Wollstein, T. Chu, J. D. Ramsey, C. Glymour, R. J. Noecker, & J. S, Schuman, "Optical coherence tomography machine learning classifiers for glaucoma detection: a preliminary study." Investigative ophthalmology & visual science, vol. 46, no. 11 pp. 4147-4152, 2005.

A. Dey, & S. K. Bandyopadhyay, "Automated glaucoma detection using support vector machine classification method," British Journal of Medicine and Medical Research vol. 11, no. 12 pp. 1, 2016.

C. W. Wu, H. Y. Chen, J. Y. Chen, & C. H. Lee, "Glaucoma detection using support vector machine method based on spectralis oct," Diagnostics vol. 12, no. 2, pp. 391, 2022.

D. K. Agrawal, B. S. Kirar, & R. B. Pachori, "Automated glaucoma detection using quasi?bivariate variational mode decomposition from fundus images," IET Image Processing vol. 13, no. 13, pp. 2401-2408, 2019.

B. S. Kirar, & D. K. Agrawal, "Current research on glaucoma detection using compact variational mode decomposition from fundus images," International Journal of Intelligent Engineering and Systems, vol. 12, no. 3, pp. 1-10, 2019.

U. Raghavendra, S. V. Bhandary, A. Gudigar, & U. R. Acharya, "Novel expert system for glaucoma identification using non-parametric spatial envelope energy spectrum with fundus images," Biocybernetics and Biomedical Engineering vol. 38, no. 1, pp. 170-180, 2018.

R. Sharma, P. Sircar, R. B. Pachori, S. V. Bhandary, & U. R. Acharya, "Automated glaucoma detection using center slice of higher order statistics," Journal of Mechanics in Medicine and Biology, vol. 19, no. 01, pp. 1940011, 2019.

P. V. Rao, R. Gayathri, and R. Sunitha. "A novel approach for design and analysis of diabetic retinopathy glaucoma detection using cup to disk ration and ANN," Procedia Materials Science, vol. 10, pp. 446-454, 2015.

T. Yoshida, A. Iwase, H. Hirasawa, H. Murata, C. Mayama, M. Araie, & R. Asaoka, "Discriminating between glaucoma and normal eyes using optical coherence tomography and the ‘Random Forests’ classifier," PloS one vol. 9, no. 8, pp. e106117, 2014.

L. Abdel-Hamid, "Glaucoma detection from retinal images using statistical and textural wavelet features," Journal of digital imaging, vol. 33, no. 1, pp. 151-158, 2020.

S. Vimal, Y. H. Robinson, M. Kaliappan, K. Vijayalakshmi, & S. Seo, "A method of progression detection for glaucoma using K-means and the GLCM algorithm toward smart medical prediction." The Journal of Supercomputing, vol. 77, no. 10, pp. 11894-11910, 2021.

J. Ayub, J. Ahmad, J. Muhammad, L. Aziz, S. Ayub, U. Akram, & I. Basit, "Glaucoma detection through optic disc and cup segmentation using K-mean clustering." In 2016 international conference on computing, electronic and electrical engineering (ICE Cube), pp. 143-147. IEEE, 2016.

S. C. Shetty, & P. Gutte, "A novel approach for glaucoma detection using fractal analysis," In 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 1-4. IEEE, 2018.

V. Mahalakshmi, and S. Karthikeyan, "Clustering based optic disc and optic cup segmentation for glaucoma detection." International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, no. 4, pp. 3756-3761, 2014.

N. Kowsalya, A. Kalyani, C. Jasmine Chalcedony, R. Sivakumar, M. Janani, and V. Rajinikanth. "An approach to extract optic-disc from retinal image using K-means clustering." In 2018 Fourth International Conference on Biosignals, Images and Instrumentation (ICBSII), pp. 206-212. IEEE, 2018.

N. Kavya, and K. V. Padmaja, "Glaucoma detection using texture features extraction," In 2017 51st Asilomar Conference on Signals, Systems, and Computers, pp. 1471-1475. IEEE, 2017.

C. B. Anusorn, W. Kongprawechnon, T. Kondo, S. Sintuwong, & K. Tungpimolrut, "Image processing techniques for glaucoma detection using the cup-to-disc ratio," Science & Technology Asia, pp. 22-34, 2013.

A. S. V. de Carvalho Junior, E. D. Carvalho, A. O. de Carvalho Filho, A. D. de Sousa, , A. C. Silva, & M. Gattass, "Automatic methods for diagnosis of glaucoma using texture descriptors based on phylogenetic diversity," Computers & Electrical Engineering, vol. 71, pp. 102-114, 2018.

S. Kavitha, and K. Duraiswamy, "An efficient decision support system for detection of glaucoma in fundus images using ANFIS," International Journal of Advances in Engineering & Technology, vol. 2, no. 1, pp. 227, 2012.

T. R. Kausu, V. P. Gopi, K. A. Wahid, W. Doma, & S. I. Niwas, "Combination of clinical and multiresolution features for glaucoma detection and its classification using fundus images," Biocybernetics and Biomedical Engineering, vol. 38, no. 2, pp. 329-341, 2018.

M. Norouzifard, A.A. Dehkordi, M. N. Dehkordi, H. Gholamhosseini, & R. Klette, "Unsupervised optic cup and optic disk segmentation for glaucoma detection by icica." In 2018 15th International Symposium on Pervasive Systems, Algorithms and Networks (I-SPAN), pp. 209-214. IEEE, 2018.

T. R. Babu, S. Devi, and R. Venkatesh, "Optic nerve head segmentation using fundus images and optical coherence tomography images for glaucoma detection," Biomedical Papers, vol. 159, no. 4, pp. 607-615, 2015.

A. A. G. Elseid, & A. O. Hamza, "Glaucoma detection using retinal nerve fiber layer texture features." Journal of Clinical Engineering, vol. 44, no. 4, pp. 180-185, 2019.

S. Maheshwari, R. B. Pachori, V. Kanhangad, S. V. Bhandary, & U. R. Acharya, "Iterative variational mode decomposition based automated detection of glaucoma using fundus images," Computers in biology and medicine, vol. 88, pp. 142-149, 2017.

C. Raja and N. Gangatharan, “Optimal hyper analytic wavelet transform for glaucoma detection in fundal retinal images,” J Electr Eng Technol., vol. 10, no. 4, pp. 1899-1909, 2015.

R. Kolar, and J. Jan, “Detection of glaucomatous eye via color fundus images using fractal dimensions,” Radio Eng., vol. 17, no. 3, pp. 109– 114, 2008.

S. L. Baxter, C. Marks, T. T. Kuo, L. Ohno-Machado, & R. N. Weinreb, "Machine learning-based predictive modeling of surgical intervention in glaucoma using systemic data from electronic health records," American journal of ophthalmology, vol. 208, pp. 30-40, 2019.

K. Sugimoto, H. Murata, H. Hirasawa, M. Aihara, C. Mayama, & R. Asaoka, "Cross-sectional study: Does combining optical coherence tomography measurements using the ‘Random Forest’decision tree classifier improve the prediction of the presence of perimetric deterioration in glaucoma suspects?." BMJ open, vol. 3, no. 10, pp. e003114, 2013.

K. Sugimoto, H. Murata, H. Hirasawa, M. Aihara, C. Mayama, & R. Asaoka, "Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning," PloS one, vol. 16, no. 4, pp. e0249856, 2021.

K. Nouri-Mahdavi, V. Mohammadzadeh, A. Rabiolo, K. Edalati, J. Caprioli, & S. Yousefi, "Prediction of visual field progression from OCT structural measures in moderate to advanced glaucoma," American Journal of Ophthalmology, vol. 226, pp. 172-181, 2021.

M. S. Eswari, & S. Balamurali, "An intelligent machine learning support system for glaucoma prediction among diabetic patients," In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 447-449. IEEE, 2021.

M. Sharifi, T. Khatibi, M. H. Emamian, S. Sadat, H. Hashemi, & A. Fotouhi, "Development of glaucoma predictive model and risk factors assessment based on supervised models," BioData Mining, vol. 14, no. 1, pp. 1-152021.

M. Zhalechian, M. P. Van Oyen, M. S. Lavieri, C. G. De Moraes, C. A. Girkin, M. A. Fazio, & J. D. Stein, "Augmenting Kalman Filter Machine Learning Models with Data from OCT to Predict Future Visual Field Loss: An Analysis Using Data from the African Descent and Glaucoma Evaluation Study and the Diagnostic Innovation in Glaucoma Study," Ophthalmology Science, vol. 2, no. 1, pp. 100097, 2022.

C. Bowd, I. Lee, M. H. Goldbaum, M. Balasubramanian, F. A. Medeiros, L. M. Zangwill, & R. N. Weinreb, "Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements," Investigative ophthalmology & visual science, vol. 53, no. 4, pp. 2382-2389, 2012.

S. Nagesh, A. Moreno, H. Ishikawa, G. Wollstein, J.S. Shuman, & J.M. Rehg, "A spatiotemporal approach to predicting glaucoma progression using a ct-hmm." In Machine Learning for Healthcare Conference, pp. 140-159. PMLR, 2019.

G. G. P. Garcia, K. Nitta, M. S. Lavieri, C. Andrews, X. Liu, E. Lobaza, & J. D. Stein, "Using Kalman filtering to forecast disease trajectory for patients with normal tension glaucoma." American journal of ophthalmology, vol. 199, pp. 111-119, 2019.

M. A. Elfattah, M. I. Waly, M. A. A. Elsoud, A. E. Hassanien, M. F. Tolba, J. Platos, & G. Schaefer, "An improved prediction approach for progression of ocular hypertension to primary open angle Glaucoma," In Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014, pp. 405-412. Springer, Cham, 2014.

I. A. Jones, M. P. Van Oyen, M. S. Lavieri, C. A. Andrews, & J. D. Stein, "Predicting rapid progression phases in glaucoma using a soft voting ensemble classifier exploiting Kalman filtering," Health Care Management Science, vol. 24, no. 4, pp. 686-701, 2021.

Y. LeCun, Y. Bengio, & G. Hinton, Deep learning. Nature, vol. 521, pp. 436–444, 2015.

Z. Obermeyer, & E. J. Emanuel, “Predicting the future — big data, machine learning, and clinical medicine,” N. Engl. J. Med, vol. 375, pp. 1216–1219, 2016.

A. Bordes, X. Glorot, J. Weston, et al. “Joint learning of words and meaning representations for open-text semantic parsing,” in: Proceedings of the AISTATS, 2012.

D.C. Ciresan, U. Meier, J. Schmidhuber, “Transfer learning for Latin and Chinese characters with deep neural networks,” in: Proceedings of the IJCNN, 2012.

J.S.J. Ren, L. Xu, “On vectorization of deep convolutional neural networks for vision tasks,” in: Proceedings of the AAAI, 2015.

T. Mikolov, I. Sutskever, K. Chen, et al., “Distributed representations of words and phrases and their compositionality,” in: Proceedings of the NIPS, 2013.

D. Ciresan, U. Meier, J. Schmidhuber, “Multi-column deep neural networks for image classification,” in: Proceedings of the CVPR, 2012.

A. Krizhevsky, I. Sutskever, G.E. Hinton, “Imagenet classification with deep convolutional neural networks,” in: Proceedings of the NIPS, 2012.

L. Deng, “A tutorial survey of architectures, algorithms, and applications for deep learning, APSIPA Trans,” Signal Inf. Process. Vol. 3 pp. e2, 2014.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9, 2015.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818–2826, 2016.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.

S. Targ, D. Almeida, and K. Lyman, “Resnet in resnet: Generalizing residual architectures,” arXiv preprint arXiv:1603.08029, 2016.

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708, 2017.

B. Al-Bander, W. Al-Nuaimy, M. A. Al-Taee, & Y. Zheng, "Automated glaucoma diagnosis using deep learning approach," In 2017 14th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 207-210. IEEE, 2017.

X. Chen, Y. Xu, S. Yan, D. W. K. Wong, T. Y. Wong, & J. Liu, "Automatic feature learning for glaucoma detection based on deep learning." In International conference on medical image computing and computer-assisted intervention, pp. 669-677. Springer, Cham, 2015.

S. Serte, & A. Serener, "A generalized deep learning model for glaucoma detection." In 2019 3rd International symposium on multidisciplinary studies and innovative technologies (ISMSIT), pp. 1-5. IEEE, 2019.

S. Yu, D. Xiao, S. Frost, & Y. Kanagasingam, "Robust optic disc and cup segmentation with deep learning for glaucoma detection," Computerized Medical Imaging and Graphics, vol. 74, 61-71, 2019.

M. Christopher, K. Nakahara, C. Bowd, J. A. Proudfoot, A. Belghith, M. H. Goldbaum, & L. M. Zangwill, "Effects of study population, labeling and training on glaucoma detection using deep learning algorithms," Translational vision science & technology, vol. 9, no. 2, pp. 27-27, 2020.

Y. Chai, H. Liu, & J. Xu, "Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models," Knowledge-Based Systems, vol. 161, pp. 147-156, 2018.

W. Liao, B. Zou, R. Zhao, Y. Chen, Z. He, & M. Zhou, "Clinical interpretable deep learning model for glaucoma diagnosis," IEEE journal of biomedical and health informatics, vol. 24, no. 5, pp.1405-1412, 2019.

M. Nawaz, T. Nazir, A. Javed, U. Tariq, H. S. Yong, M. A. Khan, & J. Cha, "An efficient deep learning approach to automatic glaucoma detection using optic disc and optic cup localization," Sensors vol. 22, no. 2, pp. 434, 2022.

Q. Abbas, "Glaucoma-deep: detection of glaucoma eye disease on retinal fundus images using deep learning," International Journal of Advanced Computer Science and Applications, vol. 8, no. 6, 2017.

A. Saxena, A. Vyas, L. Parashar, & U. Singh, "A glaucoma detection using convolutional neural network," In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 815-820. IEEE, 2020.

G. Suguna, & R. Lavanya, "Automatic identification of glaucoma using deep learning methods," Proc. 16th World Congr. Med. Health Informat. Precision Healthcare Through Informat.(MEDINFO) vol. 245, 318, 2018.

D. Natarajan, E. Sankaralingam, K. Balraj, & S. Karuppusamy, "A deep learning framework for glaucoma detection based on robust optic disc segmentation and transfer learning," International Journal of Imaging Systems and Technology, vol. 32, no. 1, pp. 230-2502022.

J. Lee, Y. K. Kim, K. H. Park, & J. W. Jeoung, “Diagnosing glaucoma with spectral-domain optical coherence tomography using deep learning classifier,” J Glaucoma, vol. 29, pp. 87–94, 2020.

A. Diaz-Pinto, S. Morales, V. Naranjo, T. Köhler, J. M. Mossi, & A. Navea, "CNNs for automatic glaucoma assessment using fundus images: an extensive validation," Biomedical engineering online, vol. 18, no. 1, pp. 1-19, 2019.M.

M. Juneja, S. Singh, N. Agarwal, S. Bali, S. Gupta, N. Thakur, & P. Jindal, "Automated detection of Glaucoma using deep learning convolution network (G-net)," Multimedia Tools and Applications, vol. 79, no. 21, pp. 15531-15553, 2020.

J. J. Gómez-Valverde, A. Antón, G. Fatti, B. Liefers, A. Herranz, A. Santos, & M. J. Ledesma-Carbayo, "Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning," Biomedical optics express, vol. 10, no. 2, pp. 892-913, 2019.

H. N. Veena, A. Muruganandham, & T. S. Kumaran, "A novel optic disc and optic cup segmentation technique to diagnose glaucoma using deep learning convolutional neural network over retinal fundus images," Journal of King Saud University-Computer and Information Sciences, 2021.

H. Fu, J. Cheng, Y. Xu, C. Zhang, D. W. K. Wong, J. Liu, & X. Cao, "Disc-aware ensemble network for glaucoma screening from fundus image," IEEE transactions on medical imaging, vol. 37, no. 11, pp. 2493-2501, 2018.

S. Gheisari, S. Shariflou, J. Phu, P. J. Kennedy, A. Agar, M. Kalloniatis, & S. M. Golzan, "A combined convolutional and recurrent neural network for enhanced glaucoma detection," Scientific reports, vol. 11, no. 1, 1-11, 2021.

M. B. Sudhan, M. Sinthuja, S. Pravinth Raja, J. Amutharaj, G. Charlyn Pushpa Latha, S. Sheeba Rachel, & Y. A. Waji, "Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model." Journal of Healthcare Engineering, 2022 (2022).

S. I. Berchuck, S. Mukherjee, & F. A. Medeiros, "Estimating rates of progression and predicting future visual fields in glaucoma using a deep variational autoencoder," Scientific reports, vol. 9, no. 1, pp. 1-122019.

S. Asano, R. Asaoka, H. Murata, Y. Hashimoto, A. Miki, K. Mori, & K. Inoue, "Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images," Scientific Reports, vol. 11, no. 1, pp. 1-10, 2021.

M. Christopher, C. Bowd, A. Belghith, M. H. Goldbaum, R. N. Weinreb, M. A. Fazio, & L. M. Zangwill, "Deep learning approaches predict glaucomatous visual field damage from OCT optic nerve head en face images and retinal nerve fiber layer thickness maps," Ophthalmology, vol. 127, no. 3, pp. 346-356, 2020.