The accuracy of the proposed method was evaluated by 10-fold cross-validation. Multiple preprocessing steps were applied for each scan to normalize the image and perform intensity correction to create 3D voxels that represent these parts accurately. A total of 814 post-mortem computed tomography scans from 619 men and 195 women, within the age range of 20–70, were collected from the National Forensic Service in South Korea. In this study, a fully automated age prediction approach was proposed by assessing 3D mandible and femur scans using deep learning. Therefore, it is logical to use automated age estimation approaches to handle large datasets. Modern computing power makes it is possible to leverage massive amounts of data to produce more reliable results. However, most existing works are laborious and requires domain-specific knowledge. ![]() ![]() Age assessment has attracted increasing attention in the field of forensics.
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