Abstract
Accurate brain tumor classification is crucial in medical imaging to ensure reliable diagnosis and effective treatment planning. This study introduces a novel double ensembling 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 (MRI), followed by deep feature extraction using transfer learning with pre-trained Vision Transformer (ViT) networks. The novelty lies in the dual-level ensembling strategy: feature-level ensembling, which integrates deep features from the top-performing ViT models, and classifier-level ensembling, which aggregates predictions from hyperparameter-optimized ML classifiers. Experiments on two public Kaggle MRI brain tumor datasets 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 of hyperparameter optimization (HPO) and advanced preprocessing techniques in improving diagnostic accuracy and reliability, advancing the integration of DL and ML for clinically relevant medical image analysis.
Abstract (translated)
精确的大脑肿瘤分类在医学成像中至关重要，以确保可靠的诊断和有效的治疗计划。本研究提出了一种新颖的双重集成框架，该框架协同结合预训练深度学习（DL）模型进行特征提取与优化后的机器学习（ML）分类器进行稳健分类。此框架包括全面处理大脑磁共振图像（MRI）的预处理和数据增强步骤，并使用迁移学习从预训练视觉变换器（ViT）网络中提取深层特征。创新之处在于双层集成策略：在特征级融合来自顶级性能ViT模型的深度特征，以及在分类器级别通过超参数优化后的ML分类器进行预测聚合。 实验基于两个公共Kaggle MRI脑肿瘤数据集显示了该方法显著超越现有最先进的方法，强调了特征和分类器融合的重要性。所提出的方法还突显了超参数优化（HPO）和高级预处理技术在提高诊断准确性和可靠性中的关键作用，推进了深度学习和机器学习在临床相关医学图像分析中的一体化应用。
URL
https://arxiv.org/abs/2506.12363
