Hybrid approach using KPSO and RLS for RBFNN design for breast cancer detection

作者：Senapati M R^*; Panda G; Dash P K

来源：Neural Computing & Applications, 2014, 24(3-4): 745-753.

DOI：10.1007/s00521-012-1286-6

• Radial basis functional neural networks (RBFNN)
• Wisconsin breast cancer (WBC)
• Pattern recognition
• Recursive least square (RLS)
• Kalman filter
• K-particle swarm optimization (KPSO)

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.

• 出版日期2014-3