基于MobileNetV3网络的龋病和根尖周炎根尖片的诊断

MobileNetV3 network-based diagnosis of caries and periapical periodontitis from periapical films

摘要

目的 研究深度学习技术智能诊断龋齿和根尖周炎的效果，初步探讨深度学习在口腔疾病诊断中的应用价值。方法 以2 298张包含健康牙齿、龋病、根尖周炎的根尖片数据集为研究对象，随机划分为1 573张训练集图像，233张验证集图像以及492张测试集图像。通过多种神经网络对比验证，选择性能较好的MobileNetV3网络模型应用于牙病诊断，并通过调整网络超参数优化模型。采用精确率、准确率、召回率和F1分数评估模型识别龋齿和根尖周炎的能力，并使用类激活热力图对网络模型性能进行可视化分析。结果 基于MobileNetV3网络模型的牙齿病变检测算法对健康牙齿、龋病和根尖周炎进行分类的精确率、召回率和准确率分别为99.42%、99.73%和99.60%，F1分数为99.57%，达到了较为理想的智能诊断效果。可视化类激活热力图也显示出网络模型能够较为准确地提取牙科病变的特征。结论 基于MobileNetV3网络模型的牙齿病变检测算法能够排除图像质量和人为因素的干扰，具有较高的诊断准确率，可满足口腔医学教学和临床应用需求。

Abstract

Objective To research the effectiveness of deep learning techniques in intelligently diagnosing dental caries and periapical periodontitis and to explore the preliminary application value of deep learning in the diagnosis of oral diseases. Methods A dataset containing 2 298 periapical films, including healthy teeth, dental caries, and periapical periodontitis, was used for the study. The dataset was randomly divided into 1 573 training images, 233 validation images, and 492 test images. By comparing various neural network models, the MobileNetV3 network model with better performance was selected for dental disease diagnosis, and the model was optimized by tuning the network hyperparameters. The accuracy, precision, recall, and F1 score were used to evaluate the model's ability to recognize dental caries and periapical periodontitis. Class activation map was used to visualization analyze the performance of the network model. Results The algorithm achieved a relatively ideal intelligent diagnostic effect with precision, recall, and accuracy of 99.42%, 99.73%, and 99.60%, respectively, and the F1 score was 99.57% for classifying healthy teeth, dental caries, and periapical periodontitis. The visualization of the class activation maps also showed that the network model can accurately extract features of dental diseases. Conclusion The tooth lesion detection algorithm based on the MobileNetV3 network model can eliminate interference from image quality and human factors and has high diagnostic accuracy, which can meet the needs of dental medicine teaching and clinical applications.

关键词

牙科病变 / 龋病 / 根尖周炎 / 根尖片 / 智能诊断 / 图像处理 / 深度学习 / MobileNetV3网络 / 类激活图 / 可视化分析

Key words

dental disease / caries / periapical periodontitis / periapical film / intelligent diagnosis / image processing / deep learning / MobileNetV3 network / class activation map / visualization analysis

Author summay

王凯欣，硕士研究生，Email：[e-mail]；

刘丰，硕士研究生，Email：[e-mail]，Tel：[phone]