Brain Tumor Detection in MRI Images using Machine Learning: Random Forest Method

Abstract

There has been a significant increase in the number of medical cases involving brain tumors in the last few years, ranking it the 10th most common form of tumor affecting children and adults. A tumor is carried on by rapid and uncontrolled cell growth in the brain. If it is not treated in the initial phases, it could prove fatal. Despite numerous significant efforts and encouraging outcomes, accurate segmentation and classification continue to be a challenge. Detection of brain tumors is significantly complicated by the distinctions in tumor position, structure, and proportions. The main disinterest of this study stays to offer investigators, comprehensive literature on Magnetic Resonance (MR) imaging’s ability to identify brain tumors. The system is designed to process input images, extracting relevant features to distinguish between various tumor types. Through rigorous training and validation, the CNN model achieves high accuracy, demonstrating its potential as a reliable tool in clinical settings. This application not only aims to improve patient outcomes but also seeks to contribute to the evolving landscape of medical imaging technology.