Abstract
Magnetic Resonance Imaging (MRI) is widely recognized as the most reliable tool for detecting tumors due to its capability to produce detailed images that reveal their presence. However, the accuracy of diagnosis can be compromised when human specialists evaluate these images. Factors such as fatigue, limited expertise, and insufficient image detail can lead to errors. For example, small tumors might go unnoticed, or overlap with healthy brain regions could result in misidentification. To address these challenges and enhance diagnostic precision, this study proposes a novel double ensembling framework, consisting of ensembled pre-trained deep learning (DL) models for feature extraction and ensembled fine-tuned hyperparameter machine learning (ML) models to efficiently classify brain tumors. Specifically, our method includes extensive preprocessing and augmentation, transfer learning concepts by utilizing various pre-trained deep convolutional neural networks and vision transformer networks to extract deep features from brain MRI, and fine-tune hyperparameters of ML classifiers. Our experiments utilized three different publicly available Kaggle MRI brain tumor datasets to evaluate the pre-trained DL feature extractor models, ML classifiers, and the effectiveness of an ensemble of deep features along with an ensemble of ML classifiers for brain tumor classification. Our results indicate that the proposed feature fusion and classifier fusion improve upon the state of the art, with hyperparameter fine-tuning providing a significant enhancement over the ensemble method. Additionally, we present an ablation study to illustrate how each component contributes to accurate brain tumor classification.
Abstract (translated)
磁共振成像（MRI）因其能够生成详细图像以揭示肿瘤的存在，而被广泛认为是检测肿瘤最可靠的工具。然而，当人类专家评估这些图像时，诊断的准确性可能会受到诸如疲劳、专业知识有限以及图像细节不足等因素的影响，导致误诊。例如，小肿瘤可能未被发现，或者与健康脑区重叠可能导致错误识别。 为了解决这些问题并提高诊断精度，本研究提出了一种新型双集合框架，包括用于特征提取的预训练深度学习（DL）模型的集合和用于高效分类脑肿瘤的微调超参数机器学习（ML）模型的集合。具体而言，我们的方法涵盖了广泛的预处理和增强、利用各种预训练的深度卷积神经网络和视觉变换器网络来从脑MRI中提取深层特征，并对ML分类器进行超参数微调。 在实验中，我们使用了三个不同的公开可用Kaggle MRI脑肿瘤数据集来评估预训练DL特征提取模型、ML分类器以及深层特征集合与ML分类器集合对于脑肿瘤分类的有效性。我们的结果显示，提出的特征融合和分类器融合改进了现有技术水平，并且超参数微调提供了比集合方法更显著的提升。此外，我们还展示了消融研究，以说明每个组件如何有助于准确地进行脑肿瘤分类。
URL
https://arxiv.org/abs/2507.12177
