融合VGG16与DenseNet121，实现脑病多类分类并可解释。
DGG-XNet: A Hybrid Deep Learning Framework for Multi-Class Brain Disease Classification with Explainable AI
- 双模型融合：VGG16提空间特征，DenseNet121增强特征复用。
- 测试准确率达91.33%，各项指标均超91%。
- 结合Grad-CAM可视化，适合临床辅助诊断场景。
阿尔茨海默病和脑肿瘤等脑部疾病的精准诊断仍是医学影像中的关键挑战。传统基于手动MRI分析的方法效率低且易出错。为此，我们提出DGG-XNet，一种融合VGG16与DenseNet121的混合深度学习模型，以增强特征提取与分类能力。DenseNet121通过密集连接促进特征复用和高效梯度传播，VGG16则提供强大的分层空间表征。两者融合实现了对神经退行性和肿瘤性脑病的鲁棒多类别分类。采用Grad-CAM可视化显著激活区域，提升模型可解释性。在BraTS 2021与Kaggle数据集组合上训练，DGG-XNet测试准确率达到91.33%，精确率、召回率和F1分数均超过91%。结果表明，DGG-XNet在神经退行性与肿瘤性脑病的计算机辅助诊断（CAD）中具有高潜力与可解释性。
原文摘要 · Abstract (English)
Accurate diagnosis of brain disorders such as Alzheimer's disease and brain tumors remains a critical challenge in medical imaging. Conventional methods based on manual MRI analysis are often inefficient and error-prone. To address this, we propose DGG-XNet, a hybrid deep learning model integrating VGG16 and DenseNet121 to enhance feature extraction and classification. DenseNet121 promotes feature reuse and efficient gradient flow through dense connectivity, while VGG16 contributes strong hierarchical spatial representations. Their fusion enables robust multiclass classification of neurological conditions. Grad-CAM is applied to visualize salient regions, enhancing model transparency. Trained on a combined dataset from BraTS 2021 and Kaggle, DGG-XNet achieved a test accuracy of 91.33\%, with precision, recall, and F1-score all exceeding 91\%. These results highlight DGG-XNet's potential as an effective and interpretable tool for computer-aided diagnosis (CAD) of neurodegenerative and oncological brain disorders.
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