用深度学习自动识别全景牙片中的四类牙病，混合模型效果最佳。
Advanced Deep Learning Techniques for Classifying Dental Conditions Using Panoramic X-Ray Images
- 用CNN提取特征+随机森林分类，提升诊断精度
- 混合模型达85.4%准确率，优于单一CNN的74.3%
- 适合临床辅助诊断系统开发，尤其需小样本优化
本研究探讨深度学习在全景牙片中自动分类牙科疾病的应用。使用包含1,512张放射影像、11,137个专家标注的四类病症数据集（补牙、蛀牙、种植体、阻生齿）。经预处理与类别平衡后，评估三种方法：自研卷积神经网络（CNN）、CNN特征提取结合传统分类器的混合模型，以及微调预训练架构。采用5折交叉验证，以准确率、精确率、召回率和F1分数为评估指标。混合CNN-随机森林模型表现最优，准确率达85.4%，超越自研CNN基线的74.3%。预训练模型中，VGG16表现最佳（82.3%），其次为Xception和ResNet50。结果表明，混合模型能更好区分形态相似病症，具备高效可靠的性能。研究提示，将CNN特征提取与集成分类器结合是实现自动化牙科诊断支持的可行路径，同时强调需更大数据集与进一步临床验证。
原文摘要 · Abstract (English)
This study investigates deep learning methods for automated classification of dental conditions in panoramic X-ray images. A dataset of 1,512 radiographs with 11,137 expert-verified annotations across four conditions fillings, cavities, implants, and impacted teeth was used. After preprocessing and class balancing, three approaches were evaluated: a custom convolutional neural network (CNN), hybrid models combining CNN feature extraction with traditional classifiers, and fine-tuned pre-trained architectures. Experiments employed 5 fold cross validation with accuracy, precision, recall, and F1 score as evaluation metrics. The hybrid CNN Random Forest model achieved the highest performance with 85.4% accuracy, surpassing the custom CNN baseline of 74.3%. Among pre-trained models, VGG16 performed best at 82.3% accuracy, followed by Xception and ResNet50. Results show that hybrid models improve discrimination of morphologically similar conditions and provide efficient, reliable performance. These findings suggest that combining CNN-based feature extraction with ensemble classifiers offers a practical path toward automated dental diagnostic support, while also highlighting the need for larger datasets and further clinical validation.
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