Authors: Pradeep Kumar, Dr. Sunil Maggu
Abstract: Brain tumors represent life-threatening neurological conditions requiring precise classification for effective treatment planning. This paper presents a Multi-Class Brain Tumor Classifier capable of distinguishing between Glioma, Meningioma, Pituitary, and No Tumor classes from MRI scans. Unlike standard binary classifiers, the system employs an Ensemble of five supervised Machine Learning algorithms — Random Forest, XGBoost, SVM, KNN, and Naive Bayes — combined through Soft Voting for robust decision-making. Texture Analysis using GLCM (Gray-Level Co-occurrence Matrix) and LBP (Local Binary Pattern) feature extraction provides explainable, biologically interpretable features rather than opaque deep-learning representations. The system is deployed as a Flask web application that automatically generates standardized PDF Medical Reports for clinical documentation. Experimental evaluation on the Kaggle Brain Tumor MRI Dataset (7,023 images) confirms that the ensemble approach achieves superior accuracy, with Random Forest and XGBoost leading individual classifier performance at 90.68% and 90.53% respectively.
