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A Comparative Study: Glaucoma Detection Using Deep Neural Networks

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Proceedings of International Conference on Big Data, Machine Learning and their Applications

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 150))

Abstract

Glaucoma is one of the most reported eye diseases which lead to complete blindness, if not treated early. Glaucoma normally associated with intraocular pressure (IOP) which gradually damages the vision field of eye. Glaucoma is an alarming threat to global health problem as it causes irreversible blindness. Evidence of glaucoma indicates that the pathogenesis of glaucoma depends on many interacting mechanism of body. Glaucoma has two main types such as open-angle and close-angle. Angle term means contacting length of iris and cornea; if the length is large, then associated disease termed as open-angle, and for shorter length, it terms as close-angle glaucoma. Glaucoma effects not only vision it also associated with hearing problem of the patients (Greco et al. in The American Journal of Medicine, 2016). In this paper, a complete review of glaucoma detection techniques based on deep learning system, CAD system and other technique is represented.

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References

  1. Greco, A., Rizzo, M. I., De Virgilio, A., Gallo, A., Fusconi, M., de Vincentiis, M. (2016). Emerging concepts in Glaucoma and review of the literature. The American Journal of Medicine (2016), https://doi.org/10.1016/j.amjmed.2016.03.038.

  2. Dervisevic, E., Pavljasevic, S., Dervisevic, A., & Kasumovic, S. S. (2016). Challenges In early Glaucoma detection. Med Arch., 70, 203–207.

    Article  Google Scholar 

  3. Spanò, A., Cedrone, C., Floris, R., Simonetti, G., & Nucci, C. (2009). Optic nerve and optic radiation neurodegeneration in patients with Glaucoma. In Vivo analysis with 3-T diffusion-tensor MR imaging. Radiology, 252(2), 496–501.

    Google Scholar 

  4. Casson, R. J., Chidlow, G., Wood, J. P., Crowston, J. G., & Goldberg, I. (2012). Definition of glaucoma: clinical and experimental concepts. Clin Exp Ophthalmol, 40(4), 341–349. PMID22356435

    Google Scholar 

  5. Cvenkel, B., & ŠketKontestabile, A. (2011). Correlation between nerve fibre layer thickness measured with spectral domain OCT and visual field in patients with different stages of glaucoma. Graefe's Archive for Clinical and Experimental Ophthalmology, 249(4), 575–584.

    Google Scholar 

  6. Mwanza, J.-C., Oakley, J. D., Budenz, D. L., Chang, R. T., O'Rese, J. K., & Feuer, W. J. (2011). Macular ganglion cell–inner plexiform layer: automated detection and thickness reproducibility with spectral domain–optical coherence tomography in glaucoma. Investigative Ophthalmology & Visual Science, 52(11), 8323–8329.

    Google Scholar 

  7. Lam, D. Y., Kaufman, P. L., Gabelt B’Ann, T., To, E. C., & Matsubara, J. A. (2003). Neurochemical correlates of cortical plasticity after unilateral elevated intraocular pressure in a primate model of glaucoma. Investigative Ophthalmology & Visual Science 44(6), 2573–2581.

    Google Scholar 

  8. Ystemucel, Y. H., Gupta, N., Zhang, Q., Mizisin, A. P., Kalichman, M. W., & Weinreb, R. N. (2006). Memantine protects neurons from shrinkage in the lateral geniculate nucleus in experimental glaucoma. Archives of Ophthalmology 124(2), 217–225.

    Google Scholar 

  9. Nouri-Mahdavi, K., Zarei, R., & Caprioli, J. (2011). Influence of visual field testing frequency on detection of glaucoma progression with trend analyses. Archives of Ophthalmology, 129(12), 1521–1527.

    Article  Google Scholar 

  10. Chandrika, S., & Nirmala, K. (2013). Analysis of Cdr detection for Glaucoma diagnosis. International Journal of Engineering Research and Applications (IJERA). ISSN: 2248-National Conference on Advanced Communication & Computing Techniques (NCACCT-19 March 2013, 23–27.

    Google Scholar 

  11. Gangwani, R. A., Chan, J., Lee, J., Kwong, A., & Lai, J. S. M. (2013). Detection of Glaucoma in a cohort of Chinese subjects with systemic hypertension. Journal of Ophthalmology, 5 (Hindawi Publishing Corporation)

    Google Scholar 

  12. Abbas, Q. (2017). Glaucoma-deep: Detection of Glaucoma eye disease on retinal fundus images using deep learning. (IJACSA) International Journal of Advanced Computer Science and Applications, 8(6).

    Google Scholar 

  13. Takayama, K., Ooto, S., Hangai, M., Ueda-Arakawa, N., Yoshida, S., Akagi, T., Ikeda, H. O., et al. (2013). Highresolution imaging of retinal nerve fiber bundles in Glaucoma using adaptive optics scanning laser ophthalmoscopy. American Journal of Ophthalmology, 155(5), 870–881.

    Google Scholar 

  14. Sharma, P., Sample, P. A., Zangwill, L. M., & Schuman, J. S. (2008). Diagnostic tools for glaucoma detection and management. Survey of Ophthalmology, 53(6), S17–S32.

    Article  Google Scholar 

  15. Golzan, S. M., Avolio, A., & Graham, S. L. (2011). Minimising retinal vessel artefacts in optical coherence tomography images. Computer Methods and Programs in Biomedicine, 104(2), 206–211.

    Google Scholar 

  16. Jan, J., Odstrcilik, J., Gazarek, J., & Kolár, R. (2012). Retinal image analysis aimed at blood vessel tree segmentation and early detection of neurallayer deterioration. Computerized Medical Imaging and Graphics, 36(6), 431–441.

    Google Scholar 

  17. Hayashi, Y., Nakagawa, T., Hatanaka, Y., Aoyama, A., Kakogawa, M., Hara, T., Fujita, H., & Yamamoto, T. (2007). Detection of retinal nerve fiber layer defects in retinal fundus imagesusing Gabor filtering. In Medical Imaging, International Society for Optics and Photonics (pp. 65142Z–65142Z).

    Google Scholar 

  18. Vermeer, K. A., Vosa, F. M., Lemijb, H. G., & Vossepoela, A. M. Detecting Glaucomatous wedge shaped defects in polarimetric images. 1361–8415/03. https://doi.org/10.1016/S1361-8415(03)00041-0.

  19. Vermeer, K. A., Reus, N. J., Vos, F. M., Vossepoel, A. M., & Lemij, H. G. Automated detection of wedgeshaped defects in polarimetric images of the retinal nerve fibre layer. In 2nd International Conference on Nature Publishing Group All rights reserved 0950-Informatics, BMEI'09 (pp. 1–4). IEEE, 2009222X/06.

    Google Scholar 

  20. Novotný, A., Odstrčilík, J., Kolář, R., & Jan, J. (2010). Texture analysis of nerve fibre layer in retinal images via local binary patterns and Gaussian Markov random fields. In 20th Biennial International EURASIP Conference BIOSIGNAL 2010 Analysis of Biomedical Signals and Images (pp. 308–315).

    Google Scholar 

  21. Zhang, Z., Liu, J., Wong, W. K., Tan, N. M., Lim, J. H., Lu, S., Li, H., Liang, Z., & Wong, T. Y. (2009). Neuro-retinal optic cup detection in glaucoma diagnosis. In 2nd International Conference on Biomedical Engineering and Informatics, Tianjin (pp. 1–4).

    Google Scholar 

  22. Murthi, A., & Madheswaran, M. (2012). Enhancement of optic cup to disc ratio detection in glaucoma diagnosis. In International Conference on Computer Communication and Informatics (ICCCI) (pp. 1–5), IEEE.

    Google Scholar 

  23. Zhang, Z., Liu, J., Wong, W. K., Tan, N. M., Lim, J. H., Lu, S., et al. (2009). Neuro-retinal optic cup detection in Glaucoma diagnosis. 978-1-4244-4134-1/09/$25.00 ©2009 IEEE.

    Google Scholar 

  24. Cheng, J., Liu, J., Lee, B. H., Wong, D. W. K., Yin, F., Aung, T., et al. (2010). Closed angle glaucoma detection in RetCam images. In Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBC) (pp. 4096–4099). IEEE.

    Google Scholar 

  25. Liu, J., Wong, D. W. K., Lim, J. H., Li, H., Tan, N. M., Zhang, Z., et al. (2009). An automatic cup-to-disc ratio measurement system for glaucoma analysis using level-set image processing. In C. T. Lim, & J. C. H. Goh (Eds.)., ICBME 2008, Proceedings (Vol. 23, pp. 559–562).

    Google Scholar 

  26. Jonas, J. B., Fernández, M. C., & Stürmer, J. Pattern of Glaucomatous neuroretinal rim loss. Ophthalmology, 100(1), 63–68. doi:10.1016/s0161.

    Google Scholar 

  27. Joshi, G. D., Sivaswamy, J., & Krishnadas, S. R. (2011). Optic disk and cup segmentation from monocular color retinal images for Glaucoma assessment. IEEE Transactions on Medical Imaging, 30(6).

    Google Scholar 

  28. Zhang, Z., Liu, J., Cherian, N. S., Sun, Y., Lim, J. H., Wong, W. K., Tan, N. M., Lu, S., Li, H., & Wong, T. Y. (2009). Convex hull based neuro-retinal optic cup ellipse optimization in glaucoma diagnosis. In Engineering in medicine and biology society, 2009. EMBC 2009 Annual International Conference of the IEEE (pp. 1441–1444). IEEE.

    Google Scholar 

  29. Song, X., Song, K., & Chen, Y. (2006). A computer-based diagnosis system for early glaucoma screening. In 27th Annual International Conference of the Engineering in Medicine and Biology Society (p. 66086611). IEEE.

    Google Scholar 

  30. Nayak, J., Acharya, R., Subbanna Bhat, P., Shetty, N., & Lim, T.-C. (2009). Automated diagnosis of Glaucoma using digital fundus images. Journal of Medical Systems 33(5), 337–346.

    Google Scholar 

  31. Cheung, C., Baskaran, M., Aung, T., & Wong, T. Y. (2012). Automated segmentation of optic disc and optic cup in fundus images for Glaucoma diagnosis. 978-1-4673-2051-1/12/$31.00 2012 IEEE.

    Google Scholar 

  32. Hatanaka, Y.,, Noudob, A., Muramatsuc, C., Sawadad, A., Harac, T., Yamamotod, T., et al. (2010). Vertical cup-to-disc ratio measurement for diagnosis of glaucoma on fundus images. In Medical Imaging 2010: Computer-Aided Diagnosis, edited by Nico Karssemeijer, Ronald MSummers, Proc. of SPIE, Vol. 7624, 76243C © 2010 SPIE CCC code: 1605-7422/10/$18 doi: https://doi.org/10.1117/12.843775.

  33. Salam, A. A., Khalil, T., Akram, M. U., Jameel, A., & Basit, I. (2016). Automated detection of glaucoma using structural and non structural features. Springer Plus, 5, 1–22.

    Article  Google Scholar 

  34. Noronhaa, K. P.,Rajendra Acharya, U., Prabhakar Nayaka, K., Martis, R. Y., Sulatha, V. B. (2013). Automated classification of glaucoma stages using higher order cumulant features. 1746–8094/$ – see front matter ©2013 Elsevier Ltd. All rights reserved.

    Google Scholar 

  35. Chen, X., Xu, Y., Wong, D. W. K., Yin Wong, T., Liu, J. (2015). Glaucoma detection based on deep convolutional neural network. IEEE EngMeSoc (pp. 715–718), Annual Conf., August 2015.

    Google Scholar 

  36. Asaoka, R., Murata, H., Iwase, A., & Araie, M. (2016). Detecting preperimetric Glaucoma with standard automated perimetry using a deep learning classifier. Ophthalmology, 123, 1974–1980.

    Article  Google Scholar 

  37. Alghamdi, H. S., Tang, H. L., Waheeb, S. A., & Peto, T. (2016). Automatic optic disc abnormality detection in fundus images: A deep learning approach. In X. Chen, M. K. Garvin, J. Liu, E. Trucco, Y. Xu (Eds.)., Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop, OMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016. 17–24.

    Google Scholar 

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Author information

Authors and Affiliations

  1. GLA University, Mathura, India

    Paresh Chandra Sau, Manish Gupta & Divesh Kumar

Authors
  1. Paresh Chandra Sau
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  2. Manish Gupta
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  3. Divesh Kumar
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Corresponding author

Correspondence to Paresh Chandra Sau .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  2. Department of Information Systems, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

    Erma Suryani

  3. Singapore Institute of Technology, Singapore, Singapore

    Andrew Keong Ng

  4. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, Uttar Pradesh, India

    K. K. Mishra

  5. Department of Electrical Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, Uttar Pradesh, India

    Nitin Singh

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Sau, P.C., Gupta, M., Kumar, D. (2021). A Comparative Study: Glaucoma Detection Using Deep Neural Networks. In: Tiwari, S., Suryani, E., Ng, A.K., Mishra, K.K., Singh, N. (eds) Proceedings of International Conference on Big Data, Machine Learning and their Applications. Lecture Notes in Networks and Systems, vol 150. Springer, Singapore. https://doi.org/10.1007/978-981-15-8377-3_8

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