Performance Improved PSO based Modified Counter Propagation Neural Network for Abnormal MR Brain Image Classification

Abnormal Magnetic Resonance (MR) brain image classification is a mandatory but challenging task in the medical field. Accurate identification of the nature of the disease is highly essential for the successful treatment planning. Automated systems are highly preferred for image classification because of its high accuracy. Artificial neural networks are one of the widely used automated techniques. Though they yield high accuracy, most of the neural networks are computationally heavy due to their iterative nature. Low speed neural classifiers are least preferred since they are practically nonfeasible. Hence, there is a significant requirement for a neural classifier which is computationally efficient and highly accurate. To satisfy these criterions, a modified Counter Propagation Neural Network (CPN) is proposed in this work which proves to be much faster than the conventional network. For further enhancement of the performance of the classifier, Particle Swarm Optimization (PSO) technique is used in conjunction with the modified CPN. Experiments are conducted on these classifiers using real-time abnormal images collected from the scan centres. These three types of classifiers are analyzed in terms of classification accuracy and convergence time period. Experimental results show promising results for the PSO based modified CPN classifier in terms of the performance measures. D.Jude Hemanth et al. 66