Machine Learning-Based Models for Magnetic Resonance Imaging (MRI)-Based Brain Tumor Classification

In the medical profession, recent technological advancements play an essential role in early detection and categorization of many diseases that cause mortality. The technique rising on daily basis for detecting illness magnetic resonance through pictures is inspection humans. Automatic (computerized) imaging has found you emergent region several diagnostic applications. Various death need to be identified such techniques technologies overcome mortality ratio. brain tumor one most common causes death. Researchers have already proposed various models classification tumors, each with its strengths weaknesses, but there still a improve process improved efficiency. However, this study, we give in-depth analysis six distinct machine learning (ML) algorithms, including Random Forest (RF), Naïve Bayes (NB), Neural Networks (NN), CN2 Rule Induction (CN2), Support Vector Machine (SVM), Decision Tree (Tree), address gap improving accuracy. On Kaggle dataset, these strategies are tested using accuracy, area under Receiver Operating Characteristic (ROC) curve, precision, recall, F1 Score (F1). training testing strengthened by 10-fold cross-validation technique. results show SVM outperforms other 95.3%