A neuro-fuzzy-classifier for a knowledge-based glaucoma monitor

  • Gudrun Zahlmann
  • Matthias Scherf
  • Aharon Wegner
Probabilistic Models and Fuzzy Logic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1211)


A knowledge-based glaucoma monitor is developed to detect critical or suspicious situations in patient's ophthalmic data sets. The decision, which type of situation occurs is made by a neuro-fuzzy classifier. The neural net part is based on a special developed feature selection algorithm and a RBF network. Fuzzy classification is realised by a fuzzy rule set combining all patient data with the classification results of the neural net classifier to the final decision.


Fuzzy Rule Feature Selection Technique Neural Network Classifier Situation Class Artificial Neural Network Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    S.E. Spenceley, D.B. Henson, D.R. Bull, Visual field analysis using artificial neural networks, Ophthal. Physiol. Opt. 14 (1994) 239–248Google Scholar
  2. [2]
    R.A. Hitchings,Perimetry-back to the future, British J. of Ohthalmol 78 (1994), 805–806.Google Scholar
  3. [3]
    G. Zahlmann, M. Obermaier, C. Ritzke, M. Scherf, Knowledge-Based Monitoring of Glaucoma Patients — a Connectionist's Approach, in J. Brender, J.P. Christensen, J.R. Scherrer, P. McNair (Eds.) Medical Informatics '96, Technology and Informatics 34 IOS-Press, 1996, S.555–559 Google Scholar
  4. [4]
    H. Bebie, Computer-assisted evaluation of visual fields, Graefe 's Arch Clin Exp Ophthalmol (1990) 228, 242–245Google Scholar
  5. [5]
    B. Lachenmayr, O.-E. Lund; 15 Jahre automatisierte Perimetrie — Wohin führt der Weg?, Klin Monatsbl Augenheilkd (1994) 205, 325–328Google Scholar
  6. [6]
    J. Flammer, The concept of visual Field indices, Graefe's Arch Clin Exp Ophthalmol (1986) 224, 389–392Google Scholar
  7. [7]
    H. Bebie, J. Flammer, Th. Bebie, The cumulative defect curve: separation of local and diffuse components of visual field damage, Graefe's Arch Clin Exp Ophthalmol (1989)227, 9–12Google Scholar
  8. [8]
    J. Weber, Perimetrische Äquivalente der Glaukomprogression, Ophthalmologe (1992) 89, 175–189Google Scholar
  9. [9]
    J.W. Sammon Jr., A nonlinear mapping for data structure analysis, IEEE Trans. Comp. C-18, (1969), 401Google Scholar
  10. [10]
    S.M. Weiss.C.A. Kulikowski,A.S. Safir, A model-based method for computer-aided medical decision-making, Artificial Intelligence II, North-Holland publ. Co 1978Google Scholar
  11. [11]
    C.E.T. Krakau, Artificial Intelligence in computerized perimetry, Doc. Ophthalmol. / Proc. of the Int. Visual Field Symp., 1986 Amsterdam 49(1987), 169–174Google Scholar
  12. [12]
    B.C. Chauhan et all., Cluster analysis in visual field quantification, Doc. Ophthalmol. 69 (1988), 25–39Google Scholar
  13. [13]
    B. Losch, Application of fuzzy sets to the diagnosis of glaucoma, Proc. IEEE-EMBS 1996Google Scholar
  14. [14]
    A. Newell, The Knowledge level, Artificial Intelligence 18(1982), 87–127Google Scholar
  15. [15]
    E.B. Baum, D. Haussler: What size net gives valid generalisation ?, Neural Computation 1(1989), 151–160Google Scholar
  16. [16]
    J. Moody and C. Darken, Learning with localized receptive fields, in Proceedings of the 1988 Connectionist Models Summer School, G. Hinton, T.Sejnowski, and D. Touretzsky, eds, 133–134 Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Gudrun Zahlmann
    • 1
  • Matthias Scherf
    • 1
  • Aharon Wegner
    • 2
  1. 1.GSF, National Research Center for Environment and HealthInstitute of Medical Informatics and Health Services ResearchNeuherberg
  2. 2.Department of Ophthalmology, Clinic rechts der IsarTechnical University of MunichMunich

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