DA-Net: A classification-guided network for dental anomaly detection from dental and maxillofacial images

IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-11-01 DOI:10.1016/j.jksuci.2024.102229
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Abstract

Dental abnormalities (DA) are frequent signs of disorders of the mouth that cause discomfort, infection, and loss of teeth. Early and reasonably priced treatment may be possible if defective teeth in the oral cavity are automatically detected. Several research works have endeavored to create a potent deep learning model capable of identifying DA from pictures. However, because of the following problems, aberrant teeth from the oral cavity are difficult to detect: 1) Normal teeth and crowded dentition frequently overlap; 2) The lesion area on the tooth surface is tiny. This paper proposes a professional dental anomaly detection network (DA-Net) to address such issues. First, a multi-scale dense connection module (MSDC) is designed to distinguish crowded teeth from normal teeth by learning multi-scale spatial information of dentition. Then, a pixel differential convolution (PDC) module is designed to perform pathological tooth recognition by extracting small lesion features. Finally, a multi-stage convolutional attention module (MSCA) is developed to integrate spatial information and channel information to obtain abnormal teeth in small areas. Experiments on benchmarks show that DA-Net performs well in dental anomaly detection and can further assist doctors in making treatment plans. Specifically, the DA-Net method performs best on multiple detection evaluation metrics: IoU, PRE, REC, and mAP. In terms of REC and mAP indicators, the proposed DA-Net method is 1.1% and 1.3% higher than the second-ranked YOLOv7 method.
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DA-Net:从牙科和颌面部图像中检测牙科异常的分类指导网络
牙齿异常(DA)是口腔疾病的常见征兆,会引起不适、感染和牙齿脱落。如果能自动检测出口腔中存在缺陷的牙齿,就可以及早进行价格合理的治疗。一些研究工作致力于创建一个强大的深度学习模型,能够从图片中识别牙齿缺损。然而,由于以下问题,口腔畸形牙难以检测:1)正常牙齿和拥挤牙经常重叠;2)牙齿表面的病变面积很小。针对这些问题,本文提出了一种专业的牙齿异常检测网络(DA-Net)。首先,设计了一个多尺度密集连接模块(MSDC),通过学习牙列的多尺度空间信息来区分拥挤牙和正常牙。然后,设计了一个像素差分卷积(PDC)模块,通过提取小病变特征来进行病牙识别。最后,开发了多级卷积注意力模块(MSCA),以整合空间信息和通道信息,从而获得小区域的异常牙齿。基准实验表明,DA-Net 在牙齿异常检测方面表现出色,可以进一步帮助医生制定治疗方案。具体来说,DA-Net 方法在多个检测评估指标上表现最佳:IoU、PRE、REC 和 mAP。在 REC 和 mAP 指标上,DA-Net 方法比排名第二的 YOLOv7 方法分别高出 1.1% 和 1.3%。
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来源期刊
CiteScore
10.50
自引率
8.70%
发文量
656
审稿时长
29 days
期刊介绍: In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.
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