Improving Brain Tumor Classification Efficacy through the Application of Feature Selection and Ensemble Classifiers

Accurate brain tumor detection is crucial due to its high mortality rate. However, existing automated methods suffer from limited accuracy and high false-positive rates. In this study, we aimed to improve brain tumor classification by comparing 17 different classifiers organized into six groups: Decision Tree (DT) Model, Support Vector Machine (SVM), Naive Bayes Classifier, Logistic Regression, Generalized Linear Model (GLM) Classifier, and Neural Network. We utilized a dataset of 3,762 Magnetic Resonance Imaging (MRI) scans of brain tumors from Kaggle, with each image having dimensions of 240 × 240 pixels and labeled as tumor or non-tumor. Our approach involved three main steps: extracting visual information using 17 predictor classes, optimizing feature extraction through weight optimization, and comparing different sets of classifier models. We evaluated the models’ performance using the confusion matrix and Receiver Operating Characteristics (ROC) curves. Our results showed that optimizing feature selection and utilizing ensemble classifiers improved the accuracy of brain tumor classification. The DT Model with ensemble classifiers emerged as the best-performing classifier, achieving an accuracy of 98.11% and an AUC of 0.99. Notably, Random Tree (RT) exhibited the highest accuracy within the ensemble classifier set, with a significant increase compared to other models. Our proposed method outperformed the standard approach, demonstrating its potential for enhancing brain tumor detection accuracy. This study contributes to the field by providing a more accurate method for detecting brain tumors, potentially enabling earlier detection and improved patient outcomes. Future research should focus on further improving brain tumor diagnosis and treatment through the application of machine learning techniques.