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Neuro-Fuzzy Nets in Medical Diagnosis: The DIAGEN Case Study of Glaucoma

  • Enrique Carmona
  • José Mira
  • Julián G. Feijoo
  • Manuel G. de la Rosa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2085)

Abstract

This work presents an approach to the automatic interpretation of the visual field to enable ophthalmology patients to be classified as glaucomatous and normal. The approach is based on a neuro-fuzzy system (NEFCLASS) that enables a set of rules to be learnt, with no a priori knowledge, and the fuzzy sets that form the rule antecedents to be tuned, on the basis of a set of training data. Another alternative is to insert knowledge (fuzzy rules) and let the system tune its antecedents, as in the previous case. Three trials are shown which demonstrate the useful application of this approach in this medical discipline, enabling a set of rule bases to be obtained which produce high sensitivity and specificity values in the classification process.

Keywords

Visual Field Fuzzy System Fuzzy Rule Rule Base Visual Field Index 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Enrique Carmona
    • 1
  • José Mira
    • 1
  • Julián G. Feijoo
    • 2
  • Manuel G. de la Rosa
    • 3
  1. 1.Dpto. de Inteligencia Artificial, Facultad de CienciasUNEDMadridSpain
  2. 2.Dpto. de Oftalmología Hospital Clínico San Carlos,Instituto de Investigaciones Oftalmológicas Ramón CastroviejoUniversidad ComplutenseMadridSpain
  3. 3.Facultad de MedicinaUniversidad de La LagunaTenerifeSpain

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