Abstract

Deep Convolution Neural Network is one of the most powerful tools to solve complex problems of image classification, image recognition, financial analysis, medical diagnosis and many similar problems. A dental panoramic image consists of collection of teeth of both upper jaw and lower jaw. Automatic classification of dental panoramic images into various tooth types such as canines, incisors, premolars and molars has been a challenging task and involves crucial role of an experienced dentist. In this paper, we propose a technique for numbering and classification of the panoramic dental images. The proposed algorithm consists of four stages namely pre-processing, segmentation, numbering and classification. The pre-processed panoramic dental images are segmented using fuzzy c-mean clustering and subjected to vertical integral projection to extract a single tooth. The image dataset consists of 400 dental panoramic images collected from various dental clinics. The 400 dental images are divided into 240 training samples and 160 testing samples. The image data set is augmented by applying various transformations. Panoramic dental images are further numbered using a universal dental numbering system. Finally, the classification is done with the help of 6-layer deep convolution neural network (DCNN) consisting of 3 convolutional neural network and 3 fully connected network. The tooth is classified as canine, incisor, molar and premolar. An accuracy of 95% has been achieved for augmented database and 92% for original dataset with the proposed algorithm. The proposed numbering and classification of dental panoramic images is useful in biomedical application and postmortem recording of dental records. In case of big calamity, the system can also assist the dentist in recording post mortem dental record that is a very lengthy and arduous task.

中文翻译：

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使用六层卷积神经网络对全景牙科图像进行编号和分类

摘要

深度卷积神经网络是解决图像分类，图像识别，财务分析，医学诊断和许多类似问题等复杂问题的最强大工具之一。牙科全景图像包括上颚和下颚的牙齿集合。将牙齿全景图像自动分类为各种牙齿类型（例如犬齿，门牙，前磨牙和磨牙）一直是一项艰巨的任务，涉及经验丰富的牙医的关键作用。在本文中，我们提出了一种用于全景牙科图像的编号和分类的技术。所提出的算法包括预处理，分割，编号和分类四个阶段。预处理的全景牙科图像使用模糊c均值聚类进行分割，并进行垂直整体投影以提取单个牙齿。图像数据集包含从各个牙科诊所收集的400张牙科全景图像。这400个牙科图像分为240个训练样本和160个测试样本。通过应用各种变换来增强图像数据集。全景牙科图像使用通用牙科编号系统进一步编号。最后，借助由3个卷积神经网络和3个全连接网络组成的6层深度卷积神经网络（DCNN）进行分类。牙齿分为犬齿，门牙，磨牙和前磨牙。利用该算法，增强数据库的准确率达到95％，原始数据集的准确率达到92％。提议的牙科全景图像编号和分类在生物医学应用和牙科记录的事后记录中很有用。在发生大灾难的情况下，该系统还可以帮助牙医记录验尸后的牙科记录，这是一项非常漫长而艰巨的任务。