医学影像中的可解释深度学习：脑肿瘤与肺炎检测
Explainable Deep Learning in Medical Imaging: Brain Tumor and Pneumonia Detection
- Department of Computer Science（计算机科学系）
- College of Business Administration（商学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出结合ResNet50和DenseNet121的可解释深度学习框架，用于MRI脑肿瘤和X射线肺炎检测，并通过Grad-CAM增强可解释性，DenseNet121表现更优且聚焦更准确。
AI中文摘要：
深度学习在改善医学影像诊断方面具有巨大潜力，然而大多数模型缺乏可解释性，这阻碍了临床信任和采用。本文提出了一种可解释的深度学习框架，用于检测MRI扫描中的脑肿瘤和胸部X射线图像中的肺炎，使用了两种领先的卷积神经网络ResNet50和DenseNet121。这些模型在公开可用的Kaggle数据集上进行了训练，该数据集包含7,023张脑部MRI图像和5,863张胸部X射线图像，取得了较高的分类性能。DenseNet121在脑肿瘤检测中持续优于ResNet50，准确率分别为94.3%对92.5%；在肺炎检测中，准确率分别为89.1%对84.4%。为了增强可解释性，集成了梯度加权类激活映射（Grad-CAM），在测试图像上叠加生成热力图可视化，指示决策过程中最具影响力的图像区域。有趣的是，尽管两个模型都产生了准确的结果，但Grad-CAM显示DenseNet121始终聚焦于核心病理区域，而ResNet50有时会将注意力分散到外围或非病理区域。将深度学习与可解释人工智能相结合，为开发可靠、可解释且临床有用的诊断工具提供了一条有前景的途径。
英文摘要：
Deep Learning (DL) holds enormous potential for improving medical imaging diagnostics, yet the lack of interpretability in most models hampers clinical trust and adoption. This paper presents an explainable deep learning framework for detecting brain tumors in MRI scans and pneumonia in chest X-ray images using two leading Convolutional Neural Networks, ResNet50 and DenseNet121. These models were trained on publicly available Kaggle datasets comprising 7,023 brain MRI images and 5,863 chest X-ray images, achieving high classification performance. DenseNet121 consistently outperformed ResNet50 with 94.3 percent vs. 92.5 percent accuracy for brain tumors and 89.1 percent vs. 84.4 percent accuracy for pneumonia. For better explainability, Gradient-weighted Class Activation Mapping (Grad-CAM) was integrated to create heatmap visualizations superimposed on the test images, indicating the most influential image regions in the decision-making process. Interestingly, while both models produced accurate results, Grad-CAM showed that DenseNet121 consistently focused on core pathological regions, whereas ResNet50 sometimes scattered attention to peripheral or non-pathological areas. Combining deep learning and explainable AI offers a promising path toward reliable, interpretable, and clinically useful diagnostic tools.
