用可解释AI提升肺癌CT筛查准确率与透明度
Explainable AI Technique in Lung Cancer Detection Using Convolutional Neural Networks
- 构建带可解释性的CNN模型，融合SHAP分析预测依据
- DenseNet121在精确率、召回率、F1值上达92%、90%、91%
- 适合临床辅助决策，尤其适用于医疗资源有限地区
早期发现肺癌对改善生存率至关重要。我们提出一种基于深度学习的自动化肺部CT影像筛查框架，集成可解释性功能。在IQ-OTH/NCCD数据集（含1,197例扫描，涵盖正常、良性、恶性三类）上，评估自定义卷积神经网络（CNN）及三种微调的迁移学习骨干网络：DenseNet121、ResNet152和VGG19。模型采用代价敏感学习缓解类别不平衡问题，通过准确率、精确率、召回率、F1分数及ROC-AUC进行评估。尽管ResNet152达到最高准确率（97.3%），但DenseNet121在精确率（92%）、召回率（90%）和F1分数（91%）上表现最佳。进一步应用Shapley加性解释（SHAP）可视化预测依据，提升临床透明度。结果表明，结合可解释性的基于CNN的方法能为肺癌筛查提供快速、准确且可理解的支持，尤其适用于资源受限环境。
原文摘要 · Abstract (English)
Early detection of lung cancer is critical to improving survival outcomes. We present a deep learning framework for automated lung cancer screening from chest computed tomography (CT) images with integrated explainability. Using the IQ-OTH/NCCD dataset (1,197 scans across Normal, Benign, and Malignant classes), we evaluate a custom convolutional neural network (CNN) and three fine-tuned transfer learning backbones: DenseNet121, ResNet152, and VGG19. Models are trained with cost-sensitive learning to mitigate class imbalance and evaluated via accuracy, precision, recall, F1-score, and ROC-AUC. While ResNet152 achieved the highest accuracy (97.3%), DenseNet121 provided the best overall balance in precision, recall, and F1 (up to 92%, 90%, 91%, respectively). We further apply Shapley Additive Explanations (SHAP) to visualize evidence contributing to predictions, improving clinical transparency. Results indicate that CNN-based approaches augmented with explainability can provide fast, accurate, and interpretable support for lung cancer screening, particularly in resource-limited settings.
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