基于改进YOLOv5s的口腔全景片牙齿病症识别算法  

Dental⁃Disease⁃Recognition Algorithm of Panoramic Oral Radiograph Based on Improved YOLOv5s

作　　者：孙召飞 俞经虎 朱行飞 陆煜 张不凡 王启蒙 Sun Zhaofei;Yu Jinghu;Zhu Xingfei;Lu Yu;Zhang Bufan;Wang Qimeng(Academy of Mechanical Engineering,Jiangnan University,Wuxi 214122,Jiangsu,China;Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment&Technology,Wuxi 214122,Jiangsu,China)

机构地区：[1]江南大学机械工程学院,江苏无锡214122 [2]江苏省食品先进制造装备技术重点实验室,江苏无锡214122

出　　处：《中国激光》2024年第15期58-67,共10页Chinese Journal of Lasers

基　　金：国家自然科学基金(51375209)。

摘　　要：目前大多数研究侧重于单一牙科疾病,而患者通常同时携带多种口腔病变。判读口腔全景片是评估患者口腔健康的重要手段,但大量的读片会过多占用医生诊断时间。因此,提出一种基于改进YOLOv5s的高效口腔全景片病症识别网络YOLO-Teeth,用以识别多种牙科病症。为了增强骨干网络的特征提取能力,使网络识别病症更准确,引入Triplet注意机制。在Neck部分采用双向特征金字塔网络(BiFPN)实现深层和浅层特征的充分融合,以确保网络更有效地处理全景片中的复杂信息。使用MPDIoU损失函数替换CIoU损失函数,以提高网络的定位精度。实验结果显示,YOLO-Teeth网络的mAP(mean average precision)值达到86.6%,相较于YOLOv5s网络提高了4.1%,并且优于其他主流检测网络。这表明YOLO-Teeth网络可协助医生快速、准确地诊断口腔病症。Objective Owing to the increasing prevalence of oral diseases,the societal demand for oral medical diagnosis has augmented steadily.This has increased the workload for oral health professionals,thus imposing higher requirements on their expertise and diagnostic efficiency.The interpretation of oral panoramic films is crucial in evaluating the oral health of patients.However,professional dentists are scarce in China,and a large number of film readings can take up too much of the doctor’s diagnostic time.The advent of artificial intelligence technology has expanded its application in the medical field,particularly in medical image analysis,and has yielded favorable results.Currently,most studies focus on individual tooth diseases.However,patients typically present multiple oral lesions simultaneously,including dental caries,apical periodontitis,furcation involvement,and impacted teeth.Owing to the complexity of these diseases,the existing technologies cannot satisfy actual clinical requirements.This study aims to leverage deep learning to recognize image features by employing a deep-learning network model to promptly and accurately identify diseased areas in oral panoramic films.The goal is to provide comprehensive results regarding conditions such as caries,periodontal disease,impacted teeth,and missing teeth.This approach aims to facilitate doctors in promptly and accurately diagnosing conditions,thereby alleviating diagnostic pressure stemming from inadequate medical resources.Methods In this study,we propose an efficient disease-recognition network named YOLO-Teeth(You only look once-teeth),which is based on YOLOv5s,to identify caries,impacted teeth,periapical periodontitis,and bifurcated root lesions.To enhance the feature-extraction capability of the backbone network,the Triplet attention mechanism is introduced such that the network recognizes the symptoms more accurately.A BiFPN module is used in the neck region to achieve a complete integration of deep and shallow features,thus ensuring that the network

关 键 词：图像处理 牙齿病症识别 深度学习 口腔全景片 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]