Hybrid method of brain tumor detection using Fuzzy K-means

This paper presents the development, simulation, and evaluation of a hybrid algorithm utilizing a combination of K-means and Fuzzy logic for brain tumor detection and segmentation. The proposed system undergoes testing with Magnetic Resonance Imaging (MRI) brain images to detect tumors. Comparative analysis reveals that the proposed system achieves a higher level of accuracy in detecting the area occupied by brain tumors in comparison with existing systems employing K-means, Fuzzy Logic, and Fuzzy C-means. The outcomes of this research pave the way for future endeavors, particularly in integrating various machine learning algorithms to further enhance tumor detection accuracy and reduce misclassified pixels.