Position Weighted Convolutional Neural Network for Unbalanced Children Caries Diagnosis | |
Zhou, Xiaojie1; Feng, Xueou5; Li, Qingming5; Yin, Qiyue5; Yang, Jun4; Yu, Guoxia1,3; Shi, Qing2 | |
刊名 | IEEE ACCESS |
2023 | |
卷号 | 11页码:77034-77044 |
关键词 | Caries diagnosis CNN transformer position embedding panoramic radiograph |
ISSN号 | 2169-3536 |
DOI | 10.1109/ACCESS.2023.3294617 |
通讯作者 | Yu, Guoxia(yuguoxia@bch.com.cn) |
英文摘要 | Panoramic radiograph is one of the most widely used inspection tools for dentists making caries diagnosis, especially for teeth that are hard to be diagnosed through visual inspection. Recently, several deep learning methods, e.g., based on convolutional neural network (CNN) or transformer network, have been proposed for automatic caries diagnosis on dental panoramic radiographs, and promising results have been achieved. However, current approaches use all the teeth equally when training their models, which results in performance degeneration because of unbalanced classification difficulties for different tooth positions. The objective of this study is to introduce a position weighted CNN to alleviate the above problem for more accurate caries diagnosis. The position weighted module evaluates and revises the output of a specially designed CNN to incorporate position information. In addition, a novel data augmentation method is used to balance data with uneven decayed and normal teeth, which is one of the reasons leading to unbalanced classification difficulty. To verify the proposed method, a children panoramic radiograph database is collected and labeled with more than 6,000 teeth. The proposed approach outperforms the state-of-the-art caries diagnosis methods with the accuracy, precision, recall, F1 and area-under-the-curve being 0.8859, 0.8875, 0.8932, 0.8903 and 0.9315, respectively. Specially, the proposed model displays higher diagnosis performance compared with two attending doctors with more than five-year clinical experience but with different diagnosis patterns, showing a potential tool for assisting dentists. |
资助项目 | Respiratory Research Project of National Clinical Research Center for Respiratory Diseases[HXZX-20210402] ; National Natural Science Foundation of China[81800925] |
WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:001041936300001 |
资助机构 | Respiratory Research Project of National Clinical Research Center for Respiratory Diseases ; National Natural Science Foundation of China |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/53828] |
专题 | 复杂系统认知与决策实验室 |
通讯作者 | Yu, Guoxia |
作者单位 | 1.Capital Med Univ, Beijing Childrens Hosp, Natl Ctr Childrens Hlth, Dept Stomatol, Beijing 100045, Peoples R China 2.Capital Med Univ, Beijing Stomatol Hosp, Beijing 100050, Peoples R China 3.Capital Med Univ, Beijing Childrens Hosp, Natl Ctr Childrens Hlth, Dept Stomatol,Natl Clin Res Ctr Resp Dis, Beijing 100045, Peoples R China 4.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China 5.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Zhou, Xiaojie,Feng, Xueou,Li, Qingming,et al. Position Weighted Convolutional Neural Network for Unbalanced Children Caries Diagnosis[J]. IEEE ACCESS,2023,11:77034-77044. |
APA | Zhou, Xiaojie.,Feng, Xueou.,Li, Qingming.,Yin, Qiyue.,Yang, Jun.,...&Shi, Qing.(2023).Position Weighted Convolutional Neural Network for Unbalanced Children Caries Diagnosis.IEEE ACCESS,11,77034-77044. |
MLA | Zhou, Xiaojie,et al."Position Weighted Convolutional Neural Network for Unbalanced Children Caries Diagnosis".IEEE ACCESS 11(2023):77034-77044. |
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