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Neural Computing and Applications
Published in: Neural Computing and Applications 1/2013

01-01-2013 | Original Article

Local linear wavelet neural network for breast cancer recognition

Authors: M. R. Senapati, A. K. Mohanty, S. Dash, P. K. Dash

Published in: Neural Computing and Applications | Issue 1/2013

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Abstract

Breast cancer is the major cause of cancer deaths in women today and it is the most common type of cancer in women. Many sophisticated algorithm have been proposed for classifying breast cancer data. This paper presents some experiments for classifying breast cancer tumor and proposes the use local linear wavelet neural network for breast cancer recognition by training its parameters using Recursive least square (RLS) approach to improve its performance. The difference of the local linear wavelet network with conventional wavelet neural network (WNN) is that the connection weights between hidden layer and output layer of conventional WNN are replaced by a local linear model. The result quality has been estimated and compared with other experiments. Results on extracted breast cancer data from University of Wisconsin Hospital Madison show that the proposed approach is very robust, effective and gives better classification.
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Footnotes
2
For m files and/or discussion, mail to manas_senapati@sify.com.
 
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Metadata
Title
Local linear wavelet neural network for breast cancer recognition
Authors
M. R. Senapati
A. K. Mohanty
S. Dash
P. K. Dash
Publication date
01-01-2013
Publisher
Springer-Verlag
Published in
Neural Computing and Applications / Issue 1/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-011-0670-y

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