DGG-XNet：一种结合可解释AI的多类脑部疾病分类混合深度学习框架
DGG-XNet: A Hybrid Deep Learning Framework for Multi-Class Brain Disease Classification with Explainable AI
- American International University-Bangladesh（美国国际大学-孟加拉）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对脑部疾病传统MRI诊断效率低易出错的问题，提出融合VGG16与DenseNet121的混合深度学习框架DGG-XNet，结合Grad-CAM实现可解释性，在组合数据集上取得91.33%的准确率，为脑部疾病计算机辅助诊断提供有效工具。
AI中文摘要：
阿尔茨海默病、脑肿瘤等脑部疾病的精准诊断仍是医学影像领域的关键挑战。传统基于人工MRI分析的方法通常效率低下且易出错。针对这一问题，我们提出DGG-XNet，这是一种融合VGG16与DenseNet121的混合深度学习模型，用于增强特征提取与分类性能。DenseNet121通过密集连接促进特征复用与高效梯度流动，VGG16则提供强大的层次化空间表征，二者融合可实现神经疾病的鲁棒多类分类。我们应用Grad-CAM可视化显著区域，提升模型透明度。在BraTS 2021与Kaggle的组合数据集上训练后，DGG-XNet的测试准确率达91.33%，精确率、召回率与F1值均超过91%。这些结果凸显了DGG-XNet作为神经退行性与肿瘤性脑部疾病计算机辅助诊断（CAD）有效且可解释工具的潜力。
英文摘要：
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.
