论文标题
用于数字牙科X射线位置分类的基于自适应增强的混合CNN模型
An Adaptive Enhancement Based Hybrid CNN Model for Digital Dental X-ray Positions Classification
论文作者
论文摘要
牙齿X光片分析是日常临床实践中诊断过程的重要组成部分。专家的解释包括牙齿检测和编号。在这个项目中，提出了基于自适应直方图均衡和卷积神经网络（CNN）的新颖解决方案，该解决方案会自动执行牙科X射线的任务。为了提高检测准确性，我们提出了三种预处理技术，以根据一些先前的域知识来补充基线CNN。首先，图像锐化和中值过滤用于消除冲动噪声，并且边缘在某种程度上增强。接下来，使用自适应直方图均衡来克服HE过度扩增噪声的问题。最后，提出了一个多CNN混合模型，以对牙齿切片的六个不同位置进行分类。结果表明，测试集的准确性和特异性超过90 \％，AUC达到0.97。此外，邀请四个牙医手动注释测试数据集（独立），然后将其与我们建议的算法获得的标签进行比较。结果表明，我们的方法可以有效地识别牙齿的X射线位置。
Analysis of dental radiographs is an important part of the diagnostic process in daily clinical practice. Interpretation by an expert includes teeth detection and numbering. In this project, a novel solution based on adaptive histogram equalization and convolution neural network (CNN) is proposed, which automatically performs the task for dental x-rays. In order to improve the detection accuracy, we propose three pre-processing techniques to supplement the baseline CNN based on some prior domain knowledge. Firstly, image sharpening and median filtering are used to remove impulse noise, and the edge is enhanced to some extent. Next, adaptive histogram equalization is used to overcome the problem of excessive amplification noise of HE. Finally, a multi-CNN hybrid model is proposed to classify six different locations of dental slices. The results showed that the accuracy and specificity of the test set exceeded 90\%, and the AUC reached 0.97. In addition, four dentists were invited to manually annotate the test data set (independently) and then compare it with the labels obtained by our proposed algorithm. The results show that our method can effectively identify the X-ray location of teeth.
