Articlegold2023

Sex estimation from Thai hand radiographs using convolutional neural networks

Pawaree Nonthasaen · Wiriya Mahikul · Thanapon Chobpenthai · Paniti Achararit

8 citations0/0 files storedForensic Science International Reports

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Forensic Anthropology and Bioarchaeology Studies Advanced Neural Network Applications Image Processing and 3D Reconstruction

Abstract

Manual analysis of hand radiographs for sex estimation is prone to biases and errors. This study addresses the need for automated methods by exploring the potential of Convolutional Neural Networks (CNNs) to accurately identify individual sex from Thai hand radiographs, overcoming limitations in data availability and variable quality. To improve dataset quality, we applied contrast limited adaptive histogram equalization (CLAHE) and Gaussian blur filter techniques to Thai hand radiographs from 385 male and 788 female individuals. We split these images into training, validation, and test sets. We also applied image augmentation techniques to increase the number of radiographs in the training dataset. Seven CNN models were trained, validated, and evaluated on 100 unseen male and female radiographs each. Among these models, the InceptionResNetV2 architecture demonstrated superior performance, achieving an accuracy of 87.50% and an F1-Score of 86.91%. Notably, this model utilized information from the 2nd to the 5th metacarpal bone and proximal phalanges in males, and from the 2nd metacarpal bone in females. Our findings provide a solid foundation for sex estimation from Thai hand radiographs, highlighting the power of CNNs in mitigating challenges associated with data quantity and quality. By automating the sex estimation process using CNNs, forensic analysis can benefit from enhanced accuracy and objectivity, enabling faster and more reliable sex assessment. We envisage that future research will build upon these findings to further improve the performance of sex estimation, contributing to advancements in forensic analysis and facilitating more effective utilization of Thai hand radiographs for sex estimation.

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References

2017Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs 2003Second to fourth digit ratio, testosterone and perceived male dominance 2011Determination of sex from the metacarpals in a Thai population 2012Sex determination by the length of metacarpals and phalanges: X-ray study on Egyptian population 2018Beyond Human Perception: Sexual Dimorphism in Hand and Wrist Radiographs Is Discernible by a Deep Learning Model 2014Estimation of sex from the metric assessment of digital hand radiographs in a Western Australian population 2022Machine Learning Techniques for Human Age and Gender Identification Based on Teeth X-Ray Images 2013Determination of sex from the proximal hand phalanges in a Thai population

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