Sex determination using the clavicle by deep learning in a Thai population.

IF 1.5 4区 医学 Q1 LAW
Medicine, Science and the Law Pub Date : 2024-01-01 Epub Date: 2023-04-17 DOI:10.1177/00258024231169233
Kewalee Pichetpan, Phruksachat Singsuwan, Pittayarat Intasuwan, Apichat Sinthubua, Patison Palee, Pasuk Mahakkanukrauh
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引用次数: 0

Abstract

Determining sex is a critical process in estimating biological profiles from skeletal remains. The clavicle is interesting in studying sex determination because it is durable to the environment, slow to decay, challenging to destroy, making the clavicle useful in autopsies and identification which can then lead to verification. The goal of this study was to use deep learning in determining sex from clavicles within the Thai population and obtain the accuracies for the validation set using a convolutional neural network (GoogLeNet). A total of 200 pairs of clavicles were obtained from 200 Thai persons (100 males and 100 females) as part of a training group. For the deep learning approach, the clavicle was photographed, and each clavicle image was submitted to the training model for sex determination. Training groups of 200 samples were made. Images of the same size were input into the training model. The percentage of the validation set accuracy was calculated from the MATLAB program. GoogLeNet was the best training model and get the result of validation set accuracy. The results of this study found accuracies for a validation set with the highest overall right lateral view of the clavicle with an accuracy of 95%. Accuracy from the validation set of each view of the clavicle can demonstrate the forensic value of sex determination. A deep learning approach with clavicles can determine the sex and is simple to utilize for forensic anthropology professionals.

通过深度学习在泰国人群中使用锁骨进行性别鉴定。
确定性别是从骨骼遗骸中估计生物特征的关键过程。锁骨对研究性别鉴定很有意义,因为它对环境的耐受性强、腐烂速度慢、破坏难度大,这使得锁骨在尸检和鉴定中非常有用,进而可以进行验证。本研究的目的是利用深度学习来确定泰国人口中锁骨的性别,并利用卷积神经网络(GoogLeNet)获得验证集的准确度。作为训练组的一部分,共从 200 名泰国人(100 名男性和 100 名女性)身上获得了 200 对锁骨。在深度学习方法中,对锁骨进行拍照,并将每张锁骨图像提交给训练模型进行性别判定。训练组共有 200 个样本。相同大小的图像被输入到训练模型中。验证集的准确率由 MATLAB 程序计算得出。GoogLeNet 是最好的训练模型,并获得了验证集准确率的结果。研究结果发现,验证集的准确率最高,锁骨右侧视图的整体准确率为 95%。锁骨各视图验证集的准确率可以证明性别鉴定的法医价值。利用锁骨的深度学习方法可以确定性别,而且对于法医人类学专业人员来说很容易使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Medicine, Science and the Law
Medicine, Science and the Law 医学-医学:法
CiteScore
2.90
自引率
6.70%
发文量
53
审稿时长
>12 weeks
期刊介绍: Medicine, Science and the Law is the official journal of the British Academy for Forensic Sciences (BAFS). It is a peer reviewed journal dedicated to advancing the knowledge of forensic science and medicine. The journal aims to inform its readers from a broad perspective and demonstrate the interrelated nature and scope of the forensic disciplines. Through a variety of authoritative research articles submitted from across the globe, it covers a range of topical medico-legal issues. The journal keeps its readers informed of developments and trends through reporting, discussing and debating current issues of importance in forensic practice.
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