Brain Tumor Detection & Classification Using FRFCM Segmentation and PSO Based Extreme Machine Learning and it’s Implementation Through Embedded System

Abstract

This research work proposes a novel Fast and robust Fuzzy C Means base (FRFCM) segmentation technique for detection of brain tumor from MR (Magnetic Resonance) image that can inform the radiologist and doctor about the details of brain tumor. This segmentation technique has been employed for rician noise removal and sharpening of the  image with  morphological reconstruction. The MR (Magnetic Resonance) Images features have been extracted through a popular Gray Level Co-occurrence Matrix (GLCM) and Discrete wavelet transform feature extraction technique. The extracted features are applied to the, proposed PSO(Particle Swarm Optimization) based extreme learning machine(ELM) for classification of the type of malignant and benign brain tumors for visual localization. Further the classification results will be compared with the existing support vector machine and relevance vector machine model. In this research work, the weights of the proposed novel multi class extreme learning machine classifier model has been updated by the PSO algorithm to increase the performance of the classifiers. To show the uniqueness of the research, further the research work proposes for implementation of detection and classification through embedded system platform which may be the product outcome of the research work. It will help the medical staff particularly for the radiologist and doctor to understand the seriousness of the tumor. Further the embedded system platform has been used to show the classification, segmentation and features through GUI (Graphical User Interface).