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Biological Gender Estimation from Panoramic Dental X‑ray Images Based on Multiple Feature Fusion Model

机译:基于多个特征融合模型的全景牙科X射线图像的生物性别估计

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摘要

In a catastrophe, the conventional biological characteristics of the victims will be destroyed. Forensic odontology is the main method to identify the victims. Estimating the gender of the victims has a significant meaning and can greatly help identify the victims. In this paper, we propose a new automatic method to the gender estimation from panoramic dental X-ray images based on improved convolutional neural network with multiple feature fusion module. Our dataset includes 19,976 panoramic dental X-ray images from Chinese patients. The method we propose can estimate 142 images per second on the conventional computing equipment and it achieves state-of-the-art performance, accuracy of 94.6% ± 0.58%, in our dataset. Our model is interpreted by perturbation-based forward propagation approaches, and the results show that focus of our method on the area of mandible and teeth is reliable which is in accordance with forensic practice.
机译:在灾难中,受害者的传统生物学特征将被摧毁。法医神话学是识别受害者的主要方法。估计受害者的性别有重要意义,可以极大地帮助识别受害者。本文在基于改进的卷积神经网络与多个特征融合模块的改进的卷积神经网络提出了一种新的自动方法。我们的数据集包括来自中国患者的19,976个全景牙科X射线图像。我们提出的方法可以在传统的计算设备上估算每秒142张图像,并且在我们的数据集中实现最先进的性能,精度为94.6%±0.58%。我们的模型由基于扰动的前向传播方法解释,结果表明,我们对下颌骨和牙齿面积的焦点是可靠的,这是符合法医实践的。

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