Skip to main content
Springer Nature Link
Log in

Detection of Glaucoma by means of ANNs

  • Methodology for Data Analysis, Task Selection and Nets Design
  • Conference paper
  • First Online:
Biological and Artificial Computation: From Neuroscience to Technology (IWANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1240))

Included in the following conference series:

  • 198 Accesses

Abstract

The use of automatic visual field classification with Artificial Neural Network (ANN) is presented. This classification is made with the purpose of helping ophthalmologist to know the possible existence of incipient glaucoma.

Various criterion's of inclusion and exclusion are set up for the patient selection. The training file is composed by 180 patterns of campimetries, and the test file by 48 patterns. In both patterns set there are visual fields of patients without any kind of pathology, and pathological visual fields: glaucoma, cataract, diabetic rethinopaty and hipertensive rethinopaty.

This work has three stages: the first stage sets up three classes: Normal, Glaucoma and Other Pathologies. The second stage sets up two classes: Glaucoma and Without Glaucoma. And the third stage sets up same classes as the second stage uses one ANN and the third stage uses two ANNs. All the ANNs used are feedforward type, and they are trained by means of backpropagation algorithm.

At the end of this document, the three stages are compared. For this purpose we study sensibility and specificity indexes, and the ratio of success and failure classifications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alfonso Antón López: Valor de las Redes Neuronales y la discrimination logística en el análisis de los defectos del campo visual, Tesis Doctoral, Facultad de Medicina, Universidad de Valladolid, 1995.

    Google Scholar 

  2. Senén Barro, José Mira: Computation Neuronal, Servicio de Publications e intercambio Científico, Universidade de Santiago de Compostela, 1995.

    Google Scholar 

  3. B. Müller, J. Reinhardt: Neural Networks. An Introduction, Springer-Verlang, Berlin, 1991.

    Google Scholar 

  4. Timothy Masters: Practical Neural Networks. Recipes in C++, Academic Press Inc., San Diego, 1993.

    Google Scholar 

  5. Murray Smith: Neural Networks for Statistical Modeling, Van Nostrans Reinhold, New York, 1993.

    Google Scholar 

  6. Stuttgart Neural Network Simulator. User Manual, Version 4.0, Report No. 6/95,Institute for Parallel and Distributed High Performance Systems (IPVR), University of Stuttgart.

    Google Scholar 

  7. Goldbaum MH, Sample PA, White H, Côté B, Raphaelian P, Fechtner RD, Weinreb RN. “Interpretation of automated perimetry for glaucoma by neural network”. Invest. Ophthalmol Vis Sci 35: 3362–3373, 1994.

    Google Scholar 

  8. Aguayo R. “Aplicación de Redes Neuronales a la Detection del Glaucoma”. Actas I Reunión ECLA005. Junio 1994. Valladolid. Spain.

    Google Scholar 

  9. Asman P, Heijl A. (1992) “Glaucoma hemifield test. Automated visual field examination”. Arch Ophthalmol 110: 812–819.

    Google Scholar 

  10. Asman P, Heijl A. (1993) “Arcuate cluster analysis in glaucoma perimetry”. Journal of Glaucoma 2(1): 13–20.

    Google Scholar 

  11. Mandava S, Caprioli J, Zulauf M. (1992) “Glaucoma pattern index to quantify glaucomatous visual field loss”. Journal of Glaucoma 1: 178–183.

    Google Scholar 

  12. Hirsbrunner HP, Fankhauser F, Jenni A, Funkhauser A. (1990) “Evaluating a perimetric expert system: experience with Octosmart”. Graefe's Arch Clin Exp Ophthalmol 228: 237–241.

    Google Scholar 

  13. Hirsbrunner HP, Fankhauser F, Funkhauser, Jenni A. (1990) “Evaluating human and automated interpretation of visual field data in perimetry”. Jpn J Ophthalmol 34: 72–80.

    Google Scholar 

  14. Kaufmann H, Flammer J, Rutishauser C. (1990) Evaluation of visual field by ophthalmologists and by Octosmart Program. 201: 104.109.

    Google Scholar 

  15. Zeyen TG, Zulauf M, Caprioli J. “Priority of test locations for automated perimetry”. Ophthalmology 1993; 100:518–23.

    Google Scholar 

  16. Anton A, JA Maquet, A Mayo, J Tapia, JC Pastor. “Evaluation of logistic discriminant analysis for interpreting visual field defects”. Ophthalmology (in press).

    Google Scholar 

  17. Katz J, Sommer A, Gaasterland DE, Anderson DR. “Comparison of analytic algorithms for detecting glaucomatous visual field loss”. Arch Ophthalmol 1991; 109: 1684–89.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universitat Autònoma de Barcelona, Bellaterra, Barcelona

    M. Reyes de los Mozos, E. Valderrama & J. Roig

  2. Centro Nacional de Microelectrónica, Bellaterra, Barcelona

    M. Reyes de los Mozos, E. Valderrama & R. Villa

  3. Instituto de Oftalmobiología Aplicada, Valladolid

    A. Antón & J. C. Pastor

Authors
  1. M. Reyes de los Mozos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. Valderrama
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Villa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. Roig
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. A. Antón
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. J. C. Pastor
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

José Mira Roberto Moreno-Díaz Joan Cabestany

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

de los Mozos, M.R., Valderrama, E., Villa, R., Roig, J., Antón, A., Pastor, J.C. (1997). Detection of Glaucoma by means of ANNs. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032559

Download citation

  • DOI: https://doi.org/10.1007/BFb0032559

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63047-0

  • Online ISBN: 978-3-540-69074-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics