Automatic Detection of Brain Tumor using Genetic Algorithms with Multiple Stages in Resonance Images

Brain tumors pose significant challenges in early detection and localization for effective treatment planning.This project aims to automatically detect brain tumors from MRI (Magnetic Reso nance Imaging) scans using a combination of Genetic Algorithms and Convolutional Neural Networks [1].The process is divided into multiple stages: first, the MRI images are pre-processed to improve clarity; then, Genetic Algorithms are used to select the most important features from the images [2].Finally, CNNs are applied to accurately classify whether the brain has a tumor or not.This approach helps doctors make faster and more reliable diagnoses, reducing the chances of human error and allowing for earlier treatment [3].First, MRI images are pre-processed to remove noise and improve quality.Then, Genetic Algorithms are applied to select the most important image features, reducing unnecessary data and improving efficiency.Finally, a CNN model is used to learn and classify the images into tumor or non-tumor categories [4].