Abstract
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.
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© 1997 Springer-Verlag Berlin Heidelberg
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Zahlmann, G., Scherf, M., Wegner, A. (1997). A neuro-fuzzy-classifier for a knowledge-based glaucoma monitor. In: Keravnou, E., Garbay, C., Baud, R., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIME 1997. Lecture Notes in Computer Science, vol 1211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0029460
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DOI: https://doi.org/10.1007/BFb0029460
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