文章：

基于MRI图像的脑肿瘤分类可解释深度集成元学习框架

Explainable Deep Ensemble Meta-Learning Framework for Brain Tumor Classification Using MRI Images

原文发布日期：30 August 2025

DOI: 10.3390/cancers17172853

类型: Article

开放获取: 是

英文摘要：

Background:Brain tumors can severely impair neurological function, leading to symptoms such as headaches, memory loss, motor coordination deficits, and visual disturbances. In severe cases, they may cause permanent cognitive damage or become life-threatening without early detection.Methods:To address this, we propose an interpretable deep ensemble model for tumor detection in Magnetic Resonance Imaging (MRI) by integrating pre-trained Convolutional Neural Networks—EfficientNetB7, InceptionV3, and Xception—using a soft voting ensemble to improve classification accuracy. The framework is further enhanced with a Light Gradient Boosting Machine as a meta-learner to increase prediction accuracy and robustness within a stacking architecture. Hyperparameter tuning is conducted using Optuna, and overfitting is mitigated through batch normalization, L2 weight decay, dropout, early stopping, and extensive data augmentation.Results:These regularization strategies significantly enhance the model’s generalization ability within the BR35H dataset. The framework achieves a classification accuracy of 99.83 on the MRI dataset of 3060 images.Conclusions:To improve interpretability and build clinical trust, Explainable Artificial Intelligence methods Grad-CAM++, LIME, and SHAP are employed to visualize the factors influencing model predictions, effectively highlighting tumor regions within MRI scans. This establishes a strong foundation for further advancements in radiology decision support systems.

摘要翻译： 

背景：脑肿瘤可严重损害神经功能，导致头痛、记忆力减退、运动协调障碍及视觉异常等症状。若未能早期发现，严重情况下可能造成永久性认知损伤甚至危及生命。 方法：为解决这一问题，我们提出一种可解释的深度集成模型用于磁共振成像中的肿瘤检测。该模型通过集成预训练的卷积神经网络——EfficientNetB7、InceptionV3和Xception，采用软投票集成策略提升分类精度。框架进一步引入轻量梯度提升机作为元学习器，在堆叠架构中增强预测准确性与鲁棒性。使用Optuna进行超参数调优，并通过批归一化、L2权重衰减、随机丢弃、早停机制及大规模数据增强等技术有效抑制过拟合。 结果：在BR35H数据集中，这些正则化策略显著提升了模型的泛化能力。该框架在包含3060张图像的MRI数据集上实现了99.83%的分类准确率。 结论：为增强模型可解释性并建立临床信任，我们采用可解释人工智能方法Grad-CAM++、LIME和SHAP对影响模型预测的因素进行可视化，有效凸显了MRI扫描中的肿瘤区域。这为放射学决策支持系统的进一步发展奠定了坚实基础。

原文链接：

Explainable Deep Ensemble Meta-Learning Framework for Brain Tumor Classification Using MRI Images