融合多种模型提升脑瘤MRI识别准确率，还能解释决策依据。
BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection
- 用CNN、ViT和GRU融合提取图像局部、全局与时间序列特征。
- 在两个数据集上达到98%准确率，优于6种主流模型。
- 首次发现像素分布影响模型性能，增强医学图像可解释性。
磁共振成像（MRI）的噪声使深度学习在肿瘤边界模糊、位置复杂的情况下难以有效识别。为此，我们提出BrainFusionNet，结合卷积神经网络（CNN）、视觉变压器（ViT）和门控循环单元（GRU），从MRI图像中提取空间上下文与序列特征，以提升脑瘤分类性能。同时集成可解释AI方法（SHAP、LIME、GradCAM），可视化并定位模型决策的关键区域。该模型在两个公开的MRI数据集上进行K折验证，准确率均达98%。与六种主流CNN及迁移学习方法对比，DenseNet121和VGG16表现最佳，准确率为96%。BrainFusionNet的创新在于：即使在小肿瘤区域或小尺寸下，也能有效提取局部与全局特征；其平衡的序列化CNN架构能捕捉浅层与深层特征；定制化的ViT可稳定梯度流，降低梯度消失风险；最后通过GRU完成分类。此外，我们分析了像素强度分布对分类的影响，发现其显著影响深度学习性能，这一发现为图像解读提供了新视角。
原文摘要 · Abstract (English)
The noise of Magnetic Resonance Imaging MRI poses challenges for Deep Learning DL when tumor boundaries are obscured tumor location and appearance are complex Therefore we develop BrainFusionNet that combines Convolutional Neural Networks CNNs Vision Transformers ViT and Gated Recurrent Units GRUs to extract spatial contextual and sequential features from MRI images for improved brain tumor classification Furthermore explainable AI such as SHAP LIME and GradCAM are integrated to visualise and highlight image regions that contribute to BrainFusionNets decisionmaking process The proposed BrainFusionNet model is evaluated on two publicly available MRI datasets Kfold validation suggests 98 accuracy on both datasets The model was compared with the six stateoftheart SOTA CNNs and transfer learning Among the SOTA CNNs DenseNet121 and VGG16 achieved the highest accuracy of 96 The novelty of BrainFusionNet is that the hybrid model effectively extracts local and global features from MRI images even in smallscale tumor regions and small tumor sizes The model has a balanced sequential CNN architecture to capture lowlevel and deeperlayer features a customized ViT that captures local features stabilizes gradient flow and reduces the risk of vanishing gradients during MRI image training The CNN and ViT outputs are fed into a GRU for final classification Furthermore we analyze pixel intensities to determine whether MRI image quality affects image classification Our findings are very novel in image interpretation as we found that the distribution of pixel intensities in MRI images affects DL performance
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