用改进U-Net和CNN骨干网络，自动分割与识别肺部CT中的肺癌。
Advanced U-Net Architectures with CNN Backbones for Automated Lung Cancer Detection and Segmentation in Chest CT Images
- 用ResNet50/VGG16/Xception作骨干网的U-Net，实现肺部区域分割。
- 肺癌分割Dice达0.9495，分类准确率最高达99.1%。
- 适合医学影像分析、AI辅助诊断研发人员参考。
本研究探讨了集成多种卷积神经网络（CNN）骨干网的U-Net架构在胸部CT图像中自动化肺癌检测与分割的有效性，以满足临床对精准诊断工具的需求。使用832张胸部CT图像（416张癌变，416张非癌变）构成平衡数据集，经对比度受限自适应直方图均衡化（CLAHE）预处理并重采样至128×128像素。采用ResNet50、VGG16和Xception作为骨干网构建U-Net模型进行肺部区域分割，随后利用基于CNN的分类器及结合CNN特征提取与传统机器学习分类器（支持向量机、随机森林、梯度提升）的混合模型，通过5折交叉验证评估性能。评估指标包括准确率、精确率、召回率、F1分数、Dice系数和ROC-AUC。结果显示，基于ResNet50的U-Net在癌变肺部分割中表现最佳（Dice: 0.9495，准确率: 0.9735），而基于VGG16的模型在非癌变肺部分割中表现最优（Dice: 0.9532，准确率: 0.9513）。分类任务中，基于U-Net-Xception的CNN模型达到99.1%准确率、99.74%召回率和99.42% F1分数；混合模型CNN-SVM-Xception达到96.7%准确率和97.88% F1分数。相较现有方法，本框架在各项指标上均持续领先。结论表明，结合U-Net与先进CNN骨干网可有效支持肺癌的分割与分类，助力早期诊断与临床决策。
原文摘要 · Abstract (English)
This study investigates the effectiveness of U-Net architectures integrated with various convolutional neural network (CNN) backbones for automated lung cancer detection and segmentation in chest CT images, addressing the critical need for accurate diagnostic tools in clinical settings. A balanced dataset of 832 chest CT images (416 cancerous and 416 non-cancerous) was preprocessed using Contrast Limited Adaptive Histogram Equalization (CLAHE) and resized to 128x128 pixels. U-Net models were developed with three CNN backbones: ResNet50, VGG16, and Xception, to segment lung regions. After segmentation, CNN-based classifiers and hybrid models combining CNN feature extraction with traditional machine learning classifiers (Support Vector Machine, Random Forest, and Gradient Boosting) were evaluated using 5-fold cross-validation. Metrics included accuracy, precision, recall, F1-score, Dice coefficient, and ROC-AUC. U-Net with ResNet50 achieved the best performance for cancerous lungs (Dice: 0.9495, Accuracy: 0.9735), while U-Net with VGG16 performed best for non-cancerous segmentation (Dice: 0.9532, Accuracy: 0.9513). For classification, the CNN model using U-Net with Xception achieved 99.1 percent accuracy, 99.74 percent recall, and 99.42 percent F1-score. The hybrid CNN-SVM-Xception model achieved 96.7 percent accuracy and 97.88 percent F1-score. Compared to prior methods, our framework consistently outperformed existing models. In conclusion, combining U-Net with advanced CNN backbones provides a powerful method for both segmentation and classification of lung cancer in CT scans, supporting early diagnosis and clinical decision-making.
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