Skull ethnic classification by combining skull auxiliary image with deep learning

Abstract

Background: China is a multi-ethnic country. It is of great significance for the skull identification to realize the skull ethnic classification through computers, which can promote the development of forensic anthropology and accelerate the exploration of national development.

Methods: In this paper, the 3D skull model is transformed into 2D auxiliary image including curvature, depth and elevation information, and then the deep learning method of the 2D auxiliary image is used for ethnic classification. We construct a convolution neural network structure inspired by VGGNet16 which has achieved excellent performance on image classification. In order to optimize the network, Adam algorithm is adopted to avoid falling into local minimum, and to ensure the stability of the algorithm with regularization terms.

Results: Experiments on 400 skull models have been conducted for ethnic classification by our method. We set different learning rates to compare the performance of the model, the highest accuracy of ethnic classification is 98.75%, which have better performance than other five classical neural network structures.

Conclusions: Deep learning based on skull auxiliary image for skull ethnic classification is an automatic and effective method with great application significance.

Author summary

Skull ethnic classification is of great significance in anthropology, forensic science and archaeology. We put forward a method of ethnic classification which combines deep learning with 2D skull auxiliary image containing depth, curvature and elevation information. The skull features are automatically extracted and classified by our network. Experimental results show that our method can obtain excellent performance of ethnic classification. In the future, we will extend the method to multi ethnic classification.

Graphical abstract

Keywords

skull auxiliary image / deep learning / skull ethnic classification / convolutional neural network

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ACKNOWLEDGEMENTS

This work was partly supported by the National Statistical Science Research Project (2020LY100), the National Natural Science Foundation of China (Nos. 62172247 and 61702293), the Key Research and Development Plan—Major Scientific and Technological Innovation Projects of ShanDong Province (No. 2019JZZY020101). We thank the support of Xianyang Hospital and People’s Hospital of Toxon County for providing CT images.

COMPLIANCE WITH ETHICS GUIDELINES

The author Huijie Sun, Junli Zhao, Chengyuan Wang, Yi Li, Niankai Zhang and Mingquan Zhou declare that they have no conflict of interests.

Our research was approved by the Institutional Review Board (IRB) of the Image Center for Brain Research, National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University and was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

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