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

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.

• Type: Conference
• Year: 2001
• Where: IWANN
• Authors: Enrique J. Carmona, José Mira, Julián G. Feijoo, Manuel G. de la Rosa
• Keywords: Automatic Interpretation, IWANN 2001, Neural Networks, Priori Knowledge, Rule Antecedents