Enhancing Brain Tumor Classification Performance Through Feature Selection In Machine Learning Models

Accurate classification of brain tumor images is crucial for accurate prognosis and effective treatment planning. This study involved an analysis of the application of machine learning in the classification of brain tumors. We employed segmentation using mU-Net, as well as feature extraction and feature selection techniques to identify the most prominent features. We identified 14 significant features using the Information Gain algorithm to classify brain tumors from 33 available features. The K-NN, SVM, Decision Tree, and Random Forest classification models were employed, and the SVM model yielded the highest performance with an accuracy of 0.929.