Comparative Analysis of Ensemble Learning Techniques for Brain Tumor Classification

This research explores the involved domain of ensemble learning techniques applied to brain tumor classification. With a specific focus on comparing the efficacy of homogenous ensemble classifiers, exemplified by the Random Forest (RF) algorithm, against heterogeneous ensemble classifiers like Voting and Stacking, this study embarks on a thorough evaluation journey. Our evaluation is not limited to accuracy measures only; instead, it surrounds recall, ROC AUC, precision, and F1-score, for better assessment of classifiers’ performance. Building upon an observed assessment performed on an appropriately selected brain tumor dataset, we provide solid empirical support demonstrating that RF not only performs better than base classifiers but also outperforms the heterogeneous ensemble methods in terms of many different performance measures. Furthermore, we discuss the specific reason that makes RF outperform other algorithms in this dataset and discuss the robustness and flexibility of this method. By unscrambling these insights, this paper serves to fill gaps in the existing knowledge regarding the utilization of ensemble knowledge acquisition techniques in the analysis of medical imaging especially within the area of brain tumor classification diagnostics.