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.
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| 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 |
