The manual detection and classification of the tumor becomes a rigorous and hectic task for the radiologists. An automatic detection and classification of brain tumor using a Hybrid Fuzzy C Means based Radial Basis Function Neural Network from the MR images is presented in this paper. The MR images has been first segmented by the K- Means algorithm and the features has been extracted from the images using GLCM (Gray Level Cooccurrence Matrix) feature extraction technique. Further in the second phase the extracted features has been aligned as input to the proposed Fuzzy C Means based Radial Basis Function Neural Network for the classification of brain tumors. The weights of the Radial Basis Function Neural Network updated by the PSO (Particle Swarm optimization) algorithm and the centers of the Radial Basis Function Neural Network are chosen by Fuzzy C Means algorithm. Also the malignant and Beignin tumor has been clustered by the Fast Fuzzy C-Means, K-Means, and KNN for visual localization. The performance of the proposed model has been compared with the Fast Fuzzy C-Means, KNN algorithm, Fuzzy c means algorithm. The result obtained from the proposed hybrid algorithm shows better classification result as compared to the previously used conventional algorithms.
