Abstract
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.
Abstract (translated)
早期检测肺癌对于改善生存结果至关重要。我们提出了一种深度学习框架，用于从胸部计算机断层扫描（CT）图像中自动筛查肺癌，并集成了可解释性功能。利用IQ-OTH/NCCD数据集（包括1,197个跨正常、良性、恶性类别的扫描），我们评估了一个定制的卷积神经网络（CNN）和三种经过微调的迁移学习骨干：DenseNet121、ResNet152和VGG19。模型通过成本敏感学习进行训练，以缓解类别不平衡，并通过准确率、精确度、召回率、F1分数和ROC-AUC指标进行评估。虽然ResNet152获得了最高的准确性（97.3%），但DenseNet121在精确度、召回率和F1得分方面提供了最佳的整体平衡（分别达到92%、90%、91%）。我们进一步应用Shapley Additive Explanations（SHAP）来可视化支持预测的证据，提高临床透明度。结果表明，结合可解释性的基于CNN的方法可以为肺癌筛查提供快速、准确且易于理解的支持，特别是在资源有限的情况下尤为有效。
URL
https://arxiv.org/abs/2508.10196
