{"title":"通过深度学习在泰国人群中使用锁骨进行性别鉴定。","authors":"Kewalee Pichetpan, Phruksachat Singsuwan, Pittayarat Intasuwan, Apichat Sinthubua, Patison Palee, Pasuk Mahakkanukrauh","doi":"10.1177/00258024231169233","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":18484,"journal":{"name":"Medicine, Science and the Law","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sex determination using the clavicle by deep learning in a Thai population.\",\"authors\":\"Kewalee Pichetpan, Phruksachat Singsuwan, Pittayarat Intasuwan, Apichat Sinthubua, Patison Palee, Pasuk Mahakkanukrauh\",\"doi\":\"10.1177/00258024231169233\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":18484,\"journal\":{\"name\":\"Medicine, Science and the Law\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medicine, Science and the Law\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/00258024231169233\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/4/17 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"LAW\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medicine, Science and the Law","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/00258024231169233","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/4/17 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"LAW","Score":null,"Total":0}
Sex determination using the clavicle by deep learning in a Thai population.
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.
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.