用注意力机制提升肺部X光片三分类准确率，支持临床可解释性。
CBAM-Enhanced DenseNet121 for Multi-Class Chest X-Ray Classification with Grad-CAM Explainability
- 在DenseNet121中加入CBAM模块，增强特征提取能力。
- 三分类准确率达84.29%，对细菌性和病毒性肺炎区分效果好。
- 结合Grad-CAM可视化，适合资源有限地区的医疗辅助诊断。
肺炎是全球儿童死亡的主要原因，尤其在孟加拉国等低资源地区，放射科医生严重短缺。现有深度学习方法大多将肺炎检测视为二分类问题，忽略了细菌性与病毒性病因的临床重要区别。本文提出CBAM-DenseNet121，一种基于迁移学习的三分类框架，用于区分正常、细菌性肺炎和病毒性肺炎。系统性基线实验表明，EfficientNetB3（73.88%）甚至低于自定义CNN基线（78.53%），这一负向发现对医学影像模型选择具有实际意义。所有实验均重复三次，使用独立随机种子（42, 7, 123），结果以均值±标准差表示。CBAM-DenseNet121在测试集上达到84.29%±1.14%准确率，各类别AUC分别为：细菌性肺炎0.9565±0.0010，正常0.9610±0.0014，病毒性肺炎0.9187±0.0037。Grad-CAM可视化显示模型关注解剖学合理的肺部区域，支持在资源受限环境中的可解释部署。
原文摘要 · Abstract (English)
Pneumonia remains a leading cause of childhood mortality worldwide, with a heavy burden in low-resource settings such as Bangladesh where radiologist availability is limited. Most existing deep learning approaches treat pneumonia detection as a binary problem, overlooking the clinically critical distinction between bacterial and viral aetiology. This paper proposes CBAM-DenseNet121, a transfer-learning framework that integrates the Convolutional Block Attention Module (CBAM) into DenseNet121 for three-class chest X-ray classification: Normal, Bacterial Pneumonia, and Viral Pneumonia. We also conduct a systematic binary-task baseline study revealing that EfficientNetB3 (73.88%) underperforms even the custom CNN baseline (78.53%) -- a practically important negative finding for medical imaging model selection. To ensure statistical reliability, all experiments were repeated three times with independent random seeds (42, 7, 123), and results are reported as mean +/- standard deviation. CBAM-DenseNet121 achieves 84.29% +/- 1.14% test accuracy with per-class AUC scores of 0.9565 +/- 0.0010, 0.9610 +/- 0.0014, and 0.9187 +/- 0.0037 for bacterial pneumonia, normal, and viral pneumonia respectively. Grad-CAM visualizations confirm that the model attends to anatomically plausible pulmonary regions for each class, supporting interpretable deployment in resource-constrained clinical environments.
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