基于深度学习的牙齿病变自动检测算法  

Automatic Detection of Dental Lesions Based on Deep Learning

作　　者：刘丰 韩民[1] 万军 刘超[2] Liu Feng;Han Min;Wang Jun;Liu Chao(School of Information Science and Engineering,Shandong University,Qingdao 266237,Shandong,China;Department of Oral and Maxillofacial Surgery,Qilu Hospital of Shandong University,Jinan 250012,Shandong,China)

机构地区：[1]山东大学信息科学与工程学院,山东青岛266237 [2]山东大学齐鲁医院口腔颌面外科,山东济南250012

出　　处：《中国激光》2022年第20期120-126,共7页Chinese Journal of Lasers

摘　　要：龋病、牙周病等口腔疾病是影响人民健康的常见病和多发病,不仅影响口腔器官功能的发挥,还常常影响全身健康,导致生活质量下降。X光片是牙科疾病诊断过程中的重要依据之一,X光片的智能化诊断对于快速准确诊断牙齿病变具有重要作用。为了实现对龋齿病变和牙根尖周病变的自动检测,本团队创建了牙齿X光片数据集,并采用YOLOV5算法对牙齿X光片中的病变区域进行了检测。检测结果表明,该算法可以有效识别牙齿中的龋齿病变和牙根尖周病变,并能检测出这两种病变的区域,检测准确率超过95%,可以满足牙齿病变智能化诊断的临床需求。Objective Since the outbreak of COVID-19,many hospitals have become overloaded with patients seeking examination,resulting in an imbalance between medical staff and patients.These high concentrations of people in hospital settings not only aggravate the risk of cross-infection among patients,but also stall the public medical system.Consequently,mild and chronic conditions cannot be treated effectively,and eventually develop into serious diseases.Therefore,the use of deep learning to accurately and efficiently analyze X-ray images for diagnostic purposes is crucial in alleviating the pressure on medical institutions during epidemics.The method developed in this study accurately detects dental X-ray lesions,thus enabling patients to self-diagnose dental conditions.Methods The method proposed in this study employs the YOLOV5algorithm to detect lesion areas on digital X-ray images and optimize the network model’s parameters.When hospitals and medical professionals collect and label training data,they use image normalization to enhance the images.Consequently,in combination with the network environment,parameters were adjusted into four modules in the YOLOV5algorithm.In the Input module,Mosaic data enhancement and adaptive anchor box algorithms are used to generate the initial box.The focus component was added to the Backbone module,and a CSP structure was implemented to determine the image features.When the obtained image features are input into the Backbone module,the FPN and PAN structures are used to realize feature fusion.Subsequently,GIOU_Loss function is applied to the Head moudule,and NMS non-maximum suppression is used to generate a regression of results.Results and Discussions The proposed YOLOV5-based neural network yields satisfactory training and testing results.The training algorithm produced a recall rate of 95%,accuracy rate of 95%,and F1score of 96%.All evaluation criteria are higher than those of the target detection algorithms of SSD and Faster-RCNN(Table 1).The network converges to smoothness aft

关 键 词：图像处理 深度学习 牙齿病变 目标检测 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]