NDLI: A neuro-fuzzy architecture in a biomedical application

Content Provider	IEEE Xplore Digital Library
Author	Bortolan, G. Fusaro, S.
Copyright Year	2004
Description	Author affiliation: Inst. of Biomed. Eng., ISIB-CNR, Padova, Italy (Bortolan, G.; Fusaro, S.)
Abstract	The aim of this paper is to present a neuro-fuzzy architecture for information processing. The main characteristic of this approach is given by its ability to process fuzzy sets and linguistic terms, preserving the simplicity and the potentiality of the connectionist method. The use of a particular notation of the trapezoidal fuzzy sets has permitted a significant enhancement and simplification of the learning algorithm. The proposed architecture has been tested in a complex task of biomedical field, the breast cancer classification. The Wisconsin breast cancer database has been considered. Two strategies have been investigated for capturing the complexity of the information present in the description of input features. The classification accuracy index was used in the validation procedure. The positive results confirm the potentiality and the effectiveness of the proposed architecture.
Sponsorship	IEEE IEEE Syst., Man, and Cybernetics Soc. Univ. of Alberta, Faculty of Eng., Dept. of Elec. & Comput. Eng. Alberta Innovation & Sci. ASERC iCORE Alberta Ingenuity (AI) Fund Nat. Res. Council (NRC) Pearson Educ. Canada J. Wiley McGraw Hill Elsevier BV Inst. of Phys. Res. Studies Press SUN Microsystems Springer-Verlag Login Brothers Canada
Starting Page	545
Ending Page	549
File Size	270578
Page Count	5
File Format	PDF
ISBN	0780383761
DOI	10.1109/NAFIPS.2004.1337359
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy sets Testing Breast cancer Information processing Biomedical engineering Collaboration Spatial databases Fuzzy set theory Fuzzy neural networks Shape
Content Type	Text
Resource Type	Article

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