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A Fused Pattern Recognition Model to Detect Glaucoma Using Retinal Nerve Fiber Layer Thickness Measurements

  • Mohammad NorouzifardEmail author
  • Ali Nemati
  • Anmar Abdul-Rahman
  • Hamid GholamHosseini
  • Reinhard Klette
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11854)

Abstract

It is estimated that approximately 1.3 billion people live with some form of vision impairment. Glaucomatous optic neuropathy is listed as the fourth major cause of vision impairment by the WHO. In 2015, an estimated 3 million people were blind due to this disease.

Structural and functional methods are utilized to detect and monitor glaucomatous damage. The relationship between these detection measures is complex and differs between individuals, especially in early glaucoma.

In this study, we aim at evaluating the relationship between retinal nerve fibre layer (RNFL) thickness and glaucoma patients. Thus, we develop a fused pattern recognition model to detect healthy vs. glaucoma patients. We also achieved an F1 score of 0.82 and accuracy of 82% using 5-fold cross-validation on a data set of 107 RNFL data from healthy eyes and 68 RNFL data from eyes with glaucoma; 25% of data have been selected randomly for testing.

The proposed fused model is based on a stack of supervised classifiers combined by an ensemble learning method to achieve a robust and generalised model for glaucoma detection in the early stages. Additionally, we implemented an unsupervised model based on K-means clustering with 80% accuracy for glaucoma screening. In this research, we have followed two purposes; first, to assist the ophthalmologists in their daily Patient examination to confirm their diagnosis, thereby increasing the accuracy of diagnosis. The second usage is glaucoma screening by optometrists in order to perform more eye tests and better glaucoma diagnosis.

Therefore, our experimental tests illustrate that having only one data set still allows us to obtain highly accurate results by applying both supervised and unsupervised models. In future, the developed model will be retested on more substantial and diverse data sets.

Keywords

Glaucoma detection RNFL Machine learning Pattern recognition Unsupervised classifier Hybrid classifier Ensemble learning 

References

  1. 1.
    Bussel, I.I., Wollstein, G., Schuman, J.S.: OCT for glaucoma diagnosis, screening and detection of glaucoma progression. Br. J. Ophthalmol. 98(Suppl. 2), ii15–ii19 (2014)CrossRefGoogle Scholar
  2. 2.
    Christopher, M., et al.: Retinal nerve fiber layer features identified by unsupervised machine learning on optical coherence tomography scans predict glaucoma progression. Invest. Ophthalmol. Vis. Sci. 59(7), 2748–2756 (2018)CrossRefGoogle Scholar
  3. 3.
    Gour, N., Khanna, P.: Automated glaucoma detection using GIST and pyramid histogram of oriented gradients (PHOG) descriptors. Pattern Recogn. Lett. (2019)Google Scholar
  4. 4.
    Heijl, A., Leske, M.C., Bengtsson, B., Hyman, L., Bengtsson, B., Hussein, M., Early Manifest Glaucoma Trial Group: Reduction of intraocular pressure and glaucoma progression: results from the Early Manifest Glaucoma Trial. Arch Ophthalmol. 120, 1268–1279 (2002)CrossRefGoogle Scholar
  5. 5.
    Johnson, C.A., Cioffi, G.A., Liebmann, J.R., Sample, P.A., Zangwill, L.M., Weinreb, R.N.: The relationship between structural and functional alterations in glaucoma: a review. Semin. Ophthalmol. 15, 221–233 (2000)CrossRefGoogle Scholar
  6. 6.
    Kass, M.A., Heuer, D.K., Higginbotham, E.J., et al.: The Ocular Hypertension Treatment Study: a randomized trial determines that topical ocular hypotensive medication delays or prevents the onset of primary open-angle glaucoma. Arch Ophthalmol. 120, 701–713 (2002)CrossRefGoogle Scholar
  7. 7.
    Lee, J., Kim, Y., Kim, J.H., Park, K.H.: Screening glaucoma with red-free fundus photography using deep learning classifier and polar transformation. J. Glaucoma 28(3), 258–264 (2019)CrossRefGoogle Scholar
  8. 8.
    Lucy, K.A., Wollstein, G.: Structural and functional evaluations for the early detection of glaucoma. Exp. Rev. Ophthalmol. 11(5), 367–376 (2016)CrossRefGoogle Scholar
  9. 9.
    Norouzifard, M., Nemati, A., Klette, R., GholamHosseini, H., Nouri-Mahdavi, K., Yousefi, S.: A hybrid machine learning model to detect glaucoma using retinal nerve fiber layer thickness measurements. Invest. Ophthalmol. Vis. Sci. 60(9), 3924 (2019)Google Scholar
  10. 10.
    Palacio-Niño, J.O.: Evaluation metrics for unsupervised learning algorithms. arXiv preprint. arXiv:1905.05667 (2019)
  11. 11.
    Patel, N.B., Sullivan-Mee, M., Harwerth, R.S.: The relationship between retinal nerve fiber layer thickness and optic nerve head neuroretinal rim tissue in glaucoma. Invest. Ophthalmol. Vis. Sci. 55(10), 6802–6816 (2014)CrossRefGoogle Scholar
  12. 12.
    Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Routledge, Boca Raton (2018)CrossRefGoogle Scholar
  13. 13.
    Sokolova, M., Japkowicz, N., Szpakowicz, S.: Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In: Sattar, A., Kang, B. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 1015–1021. Springer, Heidelberg (2006).  https://doi.org/10.1007/11941439_114CrossRefGoogle Scholar
  14. 14.
    Sommer, A., Katz, J., Quigley, H.A., et al.: Clinically detectable nerve fiber atrophy precedes the onset of glaucomatous field loss. Archive Ophthalmol. 109, 77–83 (1991)CrossRefGoogle Scholar
  15. 15.
    Tharwat, A.: Classification assessment methods. Appl. Comput. Inf. (2018)Google Scholar

Websites

  1. 16.
  2. 17.
    New Zealand: Latest stats at a glance (2015). www.blindfoundation.org.nz
  3. 18.
    World Health Organization (2018). www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment. Accessed 22 July 2019

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mohammad Norouzifard
    • 1
    Email author
  • Ali Nemati
    • 2
  • Anmar Abdul-Rahman
    • 3
  • Hamid GholamHosseini
    • 1
  • Reinhard Klette
    • 1
  1. 1.Department of Electrical and Electronic Engineering, School of Engineering, Computer, and Mathematical SciencesAuckland University of Technology (AUT)AucklandNew Zealand
  2. 2.School of Engineering and TechnologyUniversity of WashingtonTacomaUSA
  3. 3.Department of OphthalmologyCounties Manukau DHBAucklandNew Zealand

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