Detection of Glaucoma with Deep Learning

Authors(2) :-Swathi Anil, Elizabeth Isaac

Glaucoma is an ocular disorder caused due to increased uid pressure in the optic nerve. It damages the optic nerve subsequently and causes loss of vision. The available scanning methods are Heidelberg Retinal Tomography (HRT), Scanning Laser Polarimetry (SLP) and Optical Coherence Tomography (OCT). These methods are expensive and require experienced clinicians to use them. So, there is a need to diagnose glaucoma accurately with low cost. Hence, a new methodology for an automated diagnosis of glaucoma is required. Fundus images are used for the diagnosis of glaucoma. The effect of glaucoma can be reduced if we can predict glaucoma in its early stages. To predict glaucoma in its early stages a deep learning (DL) and machine learning techniques are used. with convolutional neural network and Bayesian networks. Classifiers, such as convolutional neural networks (CNNs) and Bayesian network, can infer a hierarchical representation of images to discriminate between glaucoma and non-glaucoma patterns for diagnostic decisions. These networks are compared with the parameter classification accuracy to predict which network model is best for classifying Fundus images. The features relevant for distinguishing Glaucoma patients is extracted from the fundus image. The Empirical Wavelet Transform (EWT) is employed for extracting features from fundus image . Use of wavelet transform for the feature extraction, which is faster and enables better resolution and high performance for representation and visualization of the abnormality in fundus image than other methods.

Authors and Affiliations

Swathi Anil
M Tech Student, Department of Computer Sciece, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India
Elizabeth Isaac
Assistant Professor, Department of Computer Sciece, Mar Athanasius College of Engineering, Kothamangalam, Kerala, Inida

Glaucoma, Raised Intraocular Pressure, Optic Nerve Head, Optic Cup.

  1. M. R. K. Mookiah, U. R. Acharya, C. M. Lim, A. Petznick, and J. S. Suri, "Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features," Knowledge-Based Systems, vol. 33, pp. 73–82, 2012.
  2. 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
  3. R. Bock, J. Meier, L. G. Ny´ul, J. Hornegger, and G. Michelson, "Glaucoma risk index: Automated glaucoma detection from color fundus images," Medical Image Analysis, vol. 14, no. 3, pp. 471–481, 2010.
  4. S. Kavitha, N. Zebardast, K. Palaniswamy, R. Wojciechowski, E. S. Chan, D. S. Friedman, R. Venkatesh, and P. Y. Ramulu, "Family history is a strong risk factor for prevalent angle closure in a south indian population," Ophthalmology, vol. 121, no. 11, pp. 2091–2097, 2014.
  5. M. J. Greaney, D. C. Hoffman, D. F. Garway-Heath, M. Nakla, A. L. Coleman, and J. Caprioli, "Comparison of optic nerve imaging methods to distinguish normal eyes from those with glaucoma," Investigative Ophthalmology and Visual Science, vol. 43, no. 1, pp. 140–145, 2002.
  6. 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, pp. 3846–3851, 2008.
  7. L. G. Ny´ul, "Retinal image analysis for automated glaucoma risk evaluation," Proc. SPIE: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, vol. 7497, pp. 1–9, 2009.
  8. R. A. Gafar and T. Morris, "Progress towards detection and characterisation of the optic disk in glaucoma and diabetic retinopathy," Informatics for Health and Social Care, vol. 32, no. 1, pp. 19–25, 2007.
  9. J. Staal, M. Abr`amoff, M. Niemeijer, M. Viergever, and B. V. Ginneken, "Ridge based vessel segmentation in color images of the retina," IEEE Transactions on Medical Imaging, vol. 23, no. 4, pp. 501–509, 2004.
  10. J. Nayak, U. R. Acharya, P. S. Bhat, N. Shetty, and T. C. Lim, "Automated diagnosis of glaucoma using digital fundus images," Journal of Medical Systems, vol. 33, no. 5, pp. 337–346, 2009.
  11. H. A. Quigley and A. T. Broman, "The number of people with glaucoma worldwide in 2010 and 2020," Brit. J. Ophthalmol., vol. 90, no. 3, pp. 262-267, 2006.
  12. Istvan Lazar "Retinal Microaneurysm Detection Through Local Rotating Cross- Section Profile Analysis", Proceedings on: Image Processing, VOL. 32, NO. 2, Feb 2013.

Publication Details

Published in : Volume 3 | Issue 5 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 520-525
Manuscript Number : CSEIT1835108
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Swathi Anil, Elizabeth Isaac, "Detection of Glaucoma with Deep Learning", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.520-525, May-June-2018.
Journal URL :

Follow Us

Contact Us