ORIGINAL ARTICLE Local linear wavelet neural network for breast cancer recognition M. R. Senapati • A. K. Mohanty • S. Dash • P. K. Dash Received: 16 April 2011 / Accepted: 14 June 2011 / Published online: 30 June 2011 Ó Springer-Verlag London Limited 2011 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 esti- mated 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. Keywords Local linear wavelet neural network (LLWNN)  Recursive least square (RLS)  Wisconsin breast cancer (WBC)  Minimum distance length 1 Introduction Breast cancer has become a major cause of death among women in developed countries [1, 2]. Over one ten in Europe and one in eight women in United States may be affected by breast cancer during their life time [3]. Early diagnosis requires an accurate and reliable diag- nosis procedure that allows physicians to distinguish benign breast tumors from malignant ones. Thus, finding an accurate and effective diagnosis method is very important. Biopsy is the best way to accurately determine whether the tumor is benign or malignant. However, it is invasive and expensive, and positive findings at biopsy for cancer are low, between 10 and 31% [4–6]. Much effort has been devoted over the past decade to the development and improvement of pattern classifica- tion models for breast cancer detection [7–9]. Several researchers have used statistical and artificial intelligence to successfully ‘‘predict’’ breast cancer. Basically, the objec- tive of these prediction techniques is to assign patients to either a ‘‘benign’’ group that does not have breast cancer or to a ‘‘malignant’’ group that has strong evidence of breast cancer. Recently, local linear wavelet neural networks [10, 11] have been introduced as a very effective scheme for sta- tistical pattern recognition problem and non-linear complex predictions. In this paper, a local linear wavelet neural network (LLWNN) extends the application of [10, 11] and is pro- posed for breast cancer detection, in which the connection M. R. Senapati (&)  A. K. Mohanty Department of Computer Science and Engineering, Gandhi Engineering College, Biju Patnaik University of Technology, Rourkela, Orissa 769007, India e-mail: manas_senapati@sify.com A. K. Mohanty e-mail: asw_moh@yahoo.com S. Dash Balasore College of Engineering and Technology, Balasore, Orissa 756060, India e-mail: sonali.isan@gmail.com P. K. Dash S ‘O’ A University, Bhubaneswar, Orissa 751030, India e-mail: dashpk13@yahoo.com 123 Neural Comput & Applic (2013) 22:125–131 DOI 10.1007/s00521-011-0670-y