This paper presents a novel APSO Based LLWNN (Local Linear Wavelet Neural Network) model for automatic brain tumordetection and classification.The Improved Enhanced fuzzy c means(IEnFCM) algorithm has been proposed for image segmentation and the GLCM (Gray Level Cooccurrence Matrix) feature extraction technique has been used for feature extraction from MR images.This paper aims to use the hybrid models and algorithms for classification and segmentation of brain tumors from the MR images. The extracted features have been fed as input to the proposed APSO based LLWNN model for classification of Beignin and Malignant tumors. In this research work the proposed LLWNN model weights are optimised by using APSO training which will provide unique solution to relief the hectic task of radiologist from manual detection of brain tumors from MR Images. Also the centersof the LLWNN model are also chosen by the Enhanced Fuzzy C Means algorithm and updated by the APSO algorithm. The results of proposed PSO based LLWNN model has been compared with PSO-LLWNN model, APSO-RBFNN and PSO-RBFNN model and the comparison results also presented in this paper. The experimental results obtained from the proposed model shows better classification results as compared to the existing models proposed.

Fuzzy c means algorithm (FCM), Enhanced fuzzy c means(EnFCM), RBFNN,LLWNN(Local Linear Wavelet Neural Network),APSO(Accelerated Particle Swarm Optimization),PSO

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