Swarm Optimized Modular Neural Network Based Diagnostic System for Breast Cancer Diagnosis
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 2 (4):
10 (August 2013)DOI: 10.5121/ijscai.2013.2402
Abstract
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