Paper Title
Detection of Brain Tumor using Fast and Robust Fuzzy c-means Clustering along with Support Vector Machine for Classification

Abstract
A method for segmenting images called fuzzy c-means clustering (FCM) is susceptible to noise, and it can occasionally be made better by adding local spatial information as an objective function. While reducing computational complexity and increasing precision, incorporating local spatial information calls for regular estimations of the separations between nearby pixels and cluster centers. We offer an improved Fast and Robust Fuzzy C-Means Clustering (FRFCM) method that combines membership filtering and morphology reconstruction to solve this issue.The advantages of this refined algorithm include the fact that it is free from the restrictions and limits of the standard FCM method. In this paper, we apply the presented approach to the problem of detecting brain tumors from MRI scans, and if the presence of a tumor is verified, we assess whether or not it is in the benign or malignant stage. Using a combination of morphological reconstruction, memberships filtering, rapid and resilient fuzzy C-Means for detection, and subsequent classification via Principal component analysis of the scan on many parameters and Support vector machine classifiers, we present a unique method for enhancing the accuracy, precision, and optimality of tumor identification and classification from brain MRI scans. The model outperforms earlier state-of-the-art algorithms, according to experimental data, in classifying the stage of the tumor, with maximum accuracy rates of 99.02 percent for the identification of malignant tumors and 99.67 percent for benign tumors, respectively. Keywords - Fuzzy C-means Clustering, FRFCM, Brain tumor, Support Vector Machine, Principal Component Analysis.