用自适应门控融合CNN与ViT，提升脑肿瘤MRI分类准确率。
CNN-ViT Fusion with Adaptive Attention Gate for Brain Tumor MRI Classification: A Hybrid Deep Learning Model
- 通过自适应门控动态加权CNN与ViT分支输出。
- 在Kaggle数据集上达到97.6%准确率，AUC达0.9946。
- 适合医学图像分析、深度学习融合方法研究者参考。
利用磁共振成像（MRI）早期检测和分类脑肿瘤至关重要，但医学图像特征提取困难。卷积神经网络（CNN）擅长捕捉局部纹理与空间信息，而视觉变换器（ViT）擅长捕获长程全局依赖。本文提出一种新混合架构，将SqueezeNet风格的CNN分支与MobileViT风格的全局变换器分支通过自适应注意力门机制结合。该门机制可动态学习每样本、每特征的权重，实现对局部与全局表征的上下文感知融合。模型在Brain Tumor MRI Dataset（Kaggle）上训练并评估，测试准确率达97.60，精确率为97.30，召回率为97.50，F1分数为97.40，宏平均曲线下面积（AUC）为0.9946。各项指标均优于单一CNN、ViT基线及当前主流融合方法，表明动态特征加权是医学图像分类的有效策略。
原文摘要 · Abstract (English)
Early detection and classifying brain tumors using Magnetic Resonance Imaging (MRI) images is highly important but difficult to extract in medical images. Convolutional Neural Networks (CNNs) are good at capturing both local texture and spatial information whereas Vision Transformers (ViTs) are good at capturing long-range global dependencies. We propose a new hybrid architecture that combines a SqueezeNet-style CNN branch with a MobileViT-style global transformer branch, through an Adaptive Attention Gate mechanism, in this paper. The gate learns dynamically per-sample, per-feature weights to weight the contribution of each branch, allowing context-sensitive merging of local and global representations. The proposed model has a test accuracy of 97.60, a precision of 97.30, a recall of 97.50, an F1-score of 97.40, and a macro-average area under the curve (AUC) of 0.9946 with a trained and evaluated on the Brain Tumor MRI Dataset (Kaggle). These scores are higher than single CNN and ViT baselines, and current competitive fusion methods, showing that dynamic feature weighting is an effective way to classify medical images.
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