Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classification

Abstract

Accurate classification of brain tumors in medical imaging is crucial to ensure reliable diagnoses and effective treatment planning. This study introduces a novel double ensemble framework that synergistically combines pre-trained Deep Learning (DL) models for feature extraction with optimized Machine Learning (ML) classifiers for robust classification. The framework incorporates comprehensive preprocessing and data augmentation of brain Magnetic Resonance Images (MRIs), followed by deep feature extraction based on transfer learning using pre-trained Vision Transformer (ViT) networks. The novelty of our approach lies in its dual-level ensemble strategy: we employ a feature-level ensemble, which integrates deep features from the top-performing ViT models, and a classifier-level ensemble, which aggregates predictions from various hyperparameter-optimized ML classifiers. Experiments on two public MRI brain tumor datasets from Kaggle demonstrate that this approach significantly surpasses state-of-the-art methods, underscoring the importance of feature and classifier fusion. The proposed methodology also highlights the critical roles that hyperparameter optimization and advanced preprocessing techniques can play in improving the diagnostic accuracy and reliability of medical image analysis, advancing the integration of DL and ML in this vital, clinically relevant task.