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Please use this identifier to cite or link to this item: http://idr.iitbbs.ac.in/jspui/handle/2008/483
Title: Hybrid approach using KPSO and RLS for RBFNN design for breast cancer detection
Authors: Senapati M.R.
Panda G.
Dash P.K.
Keywords: K-particle swarm optimization (KPSO)
Kalman filter
Pattern recognition
Radial basis functional neural networks (RBFNN)
Recursive least square (RLS)
Wisconsin breast cancer (WBC)
Issue Date: 2014
Citation: 7
Abstract: Neural networks have been employed in many medical applications including breast cancer classification. Innovation in diagnostic features of tumors may play a central role in development of new treatment methods for earliest stage of breast cancer detection. This study proposes a new hybrid for breast cancer detection by extending the application of a variation of particle swarm optimization called K-particle swarm optimization (KPSO). In this paper, the centers and variances of radial basis functional neural network are initialized by KPSO and then updated using back propagation. The weights are updated using recursive least square instead of back propagation. The results are compared with some recently developed techniques. It is found that the proposed technique provides more accurate result and better classification as compared to some other techniques. � 2012 Springer-Verlag London.
URI: http://dx.doi.org/1007/s00521-012-1286-6
http://10.10.32.48:8080/jspui/handle/2008/483
Appears in Collections:Research Publications

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