A neuro-fuzzy approach as medical diagnostic interface
- In contrast to the symbolic approach, neural networks seldom are designed to explain what they have learned. This is a major obstacle for its use in everyday life. With the appearance of neuro-fuzzy systems which use vague, human-like categories the situation has changed. Based on the well-known mechanisms of learning for RBF networks, a special neuro-fuzzy interface is proposed in this paper. It is especially useful in medical applications, using the notation and habits of physicians and other medically trained people. As an example, a liver disease diagnosis system is presented.
Author: | Rüdiger W. BrauseGND, F. Friedrich |
---|---|
URN: | urn:nbn:de:hebis:30-79176 |
Document Type: | Article |
Language: | English |
Date of Publication (online): | 2010/09/08 |
Year of first Publication: | 2000 |
Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Release Date: | 2010/09/08 |
Page Number: | 6 |
Note: | Postprint, zuerst in: European Symposium on Artificial Neural Networks, ESANN 2000, D-Facto, Brussels, 2000, S. 201-206 |
Source: | European Symposium on Artificial Neural Networks, ESANN 2000, D-Facto, Brussels, 2000, pp. 201-206 |
HeBIS-PPN: | 227737415 |
Institutes: | Informatik und Mathematik / Informatik |
Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
Sammlungen: | Universitätspublikationen |
Licence (German): | Deutsches Urheberrecht |