BrainFusionNet：一种用于理解MRI图像局部、全局和序列特征以改进脑肿瘤检测的深度学习与XAI模型
BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection
AI总结：
提出BrainFusionNet混合模型，结合CNN、ViT和GRU提取MRI空间、上下文和序列特征，并集成SHAP、LIME和GradCAM进行可解释性分析，在公开数据集上达到98%准确率，优于SOTA CNN。
摘要：
磁共振成像（MRI）的噪声给深度学习（DL）带来挑战，当肿瘤边界模糊、肿瘤位置和外观复杂时尤其如此。因此，我们开发了BrainFusionNet，它结合卷积神经网络（CNN）、视觉变换器（ViT）和门控循环单元（GRU），从MRI图像中提取空间、上下文和序列特征，以改进脑肿瘤分类。此外，集成了可解释AI（如SHAP、LIME和GradCAM），以可视化和突出显示有助于BrainFusionNet决策过程的图像区域。所提出的BrainFusionNet模型在两个公开MRI数据集上进行了评估，K折验证表明在两个数据集上准确率均达到98%。该模型与六种最先进的（SOTA）CNN和迁移学习进行了比较。在SOTA CNN中，DenseNet121和VGG16达到了96%的最高准确率。BrainFusionNet的新颖之处在于，该混合模型能够有效提取MRI图像的局部和全局特征，即使在小尺度肿瘤区域和肿瘤尺寸较小的情况下也是如此。该模型具有平衡的序列CNN架构，以捕获低层和深层特征；以及定制的ViT，可捕获局部特征、稳定梯度流并降低MRI图像训练期间梯度消失的风险。CNN和ViT的输出被馈送到GRU以进行最终分类。此外，我们分析像素强度以确定MRI图像质量是否影响图像分类。我们的发现在图像解释方面非常新颖，因为我们发现MRI图像中像素强度的分布会影响DL性能。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
