Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Information Systems Department, HECI School, Dar Alhekma University, Jeddah, Saudi Arabia.
Comput Intell Neurosci. 2022 Apr 30;2022:3500552. doi: 10.1155/2022/3500552. eCollection 2022.
An important aspect of the diagnosis procedure in daily clinical practice is the analysis of dental radiographs. This is because the dentist must interpret different types of problems related to teeth, including the tooth numbers and related diseases during the diagnostic process. For panoramic radiographs, this paper proposes a convolutional neural network (CNN) that can do multitask classification by classifying the X-ray images into three classes: cavity, filling, and implant. In this paper, convolutional neural networks are taken in the form of a NASNet model consisting of different numbers of max-pooling layers, dropout layers, and activation functions. Initially, the data will be augmented and preprocessed, and then, the construction of a multioutput model will be done. Finally, the model will compile and train the model; the evaluation parameters used for the analysis of the model are loss and the accuracy curves. The model has achieved an accuracy of greater than 96% such that it has outperformed other existing algorithms.
在日常临床实践中，诊断程序的一个重要方面是分析牙科射线照片。这是因为牙医在诊断过程中必须解释与牙齿有关的不同类型的问题，包括牙号和相关疾病。对于全景射线照片，本文提出了一种卷积神经网络（CNN），可以通过将 X 射线图像分为三类（龋齿、填充和种植体）来进行多任务分类。在本文中，卷积神经网络采用了由不同数量的最大池化层、辍学层和激活函数组成的 NASNet 模型的形式。最初，将对数据进行扩充和预处理，然后构建一个多输出模型。最后，将编译和训练模型；用于分析模型的评估参数是损失和准确性曲线。该模型的准确率大于 96%，优于其他现有算法。
