Swarm Optimized Modular Neural Network Based Diagnostic System for Breast Cancer Diagnosis
B. Pandey, and N. Garg.
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) 2 (4): 10 (August 2013)

Artificial Neural Networks have long been considered a simple yet powerful and elegant paradigm for solving problems related to Pattern Recognition, Machine Learning and Knowledge Discovery. However, performance of traditional, monolithic neural network based systems, suffers when faced with complex problems which involve a large number of decision variables or dimensions. Also, performance of any such system depends on the architecture of the neural network involved. The architecture usually remains sub-optimal as human expertise is generally used to design the optimal architecture. In this paper, we describe how the twin paradigms of modularity and swarm intelligence based optimization could be successfully used to overcome these concerns. Here, instead of using a single monolithic expert, we use a modular neural network where several independent neural network experts individually work upon the inputs and give their outputs which is then integrated using an Integrator (here, a Fuzzy C-Means Integrator). Also, swarm intelligence has been used to determine the connections in each individual expert for achieving an optimized architecture for each expert. This approach has been used for the diagnosis of breast cancer disease. Experimental results show that the proposed approach gives a better diagnostic ability than those of other traditional methods used
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