Sex estimation of the pelvis by deep learning of two-dimensional depth images generated from homologous models of three-dimensional computed tomography images
The utility of convolutional neural networks (CNNs) for sex estimation of the pelvis was evaluated using depth images generated from reconstructed three-dimensional (3D) computed tomography images. The 3D volume data were normalized by a homologous modeling technique to create polygon data with iden...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-12-01
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| Series: | Forensic Science International: Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665910720300785 |