Gender Classification With Hand-Wrist Radiographs Using the Deep Learning Method

Year 2025, , 2 - 7, 20.01.2025

Özkan Miloğlu Nida Kumbasar Zeynep Turanli Tosun Mustafa Taha Güller İbrahim Yücel Özbek

https://doi.org/10.17567/currresdentsci.1618860

Abstract

Objective: Before dental procedures, hand-wrist radiographs are used to plan treatment time and determine skeletal maturity. This study aims to determine gender from hand-wrist radiographs using different deep-learning methods.
Methods: The left hand-wrist radiographs of 1044 individuals (534 males and 510 females) were pre-processed to clarify the image and adjust the contrast. In the gender classification problem, AlexNet, VGG16 and VGG19 transfer learning methods were both used as separate classifiers, and the features taken from these methods were combined and given to the support vector machine (SVM) classifier.
Results: The results revealed that image analysis and deep learning techniques provided 91.1% accuracy in gender determination.
Conclusion: Hand-wrist radiographs exhibited sexual dimorphism and could be used in gender prediction.
Keywords: Deep learning; İmage analysis; Hand-wrist radiographs; Gender determination

Keywords

deep learning, image analysis, ; hand-wrist radiographs

References

• 1. Malatong Y, Intasuwan P, Palee P, Sinthubua A, Mahakkanukrauh P. Deep learning and morphometric approach for sex determination of the lumbar vertebrae in a Thai population. Med Sci Law. 2023;63(1):14-21.
• 2. Kosif R, Kürkçüoğlu A. Yüz Açılarından Cinsiyet Tayini. Adli Tıp Bülteni. 2022;27(2):122-128.
• 3. Klales AR. Secular change in morphological pelvic traits used for sex estimation. J Forensic Sci. 2016;61(2):295-301.
• 4. Wibisono A, Saputri MS, Mursanto P, et al. Deep learning and classic machine learning approach for automatic bone age assessment. 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) IEEE. 2019; 235-240.
• 5. Bengio Y, LeCun Y. Scaling learning algorithms towards AI. Large-scale Kernel Machines. 2007; 34(5):1-41. https://doi.org/10.7551/mitpress/7496.003.0016
• 6. Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiol. 2018;287(1):313-322.
• 7. Zakiroğlu N. Yapay zeka teknikleri kullanarak kemik yaşı tespiti üzerinde bir uygulama. Fen Bilimleri Enstitüsü. 2019.
• 8. Miloglu O, Guller MT, Turanli Tosun Z. The use of artificial intelligence in dentistry practices. Eurasian J Med. 2022;54 (Supp11): 34-42.
• 9. Zuiderveld K. Contrast limited adaptive histogram equalization. Graph Gems. 1994;474-485.
• 10. Pietka E, Gertych A, Pospiech S, Cao F, Huang HK, Gilsanz V. Computer-assisted bone age assessment: image preprocessing and epiphyseal/metaphyseal ROI extraction. IEEE Trans Med Imaging. 2001;20(8):715-729.
• 11. Yune S, Lee H, Kim M, Tajmir SH, Gee MS, Do S. Beyond human perception: sexual dimorphism in hand and wrist radiographs is discernible by a deep learning model. J Digital Imaging. 2019;32:665-671.
• 12. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Informat Processing Systems. 2012;25:1097-1105.
• 13. Simonyan K, Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014.
• 14. Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995; 20:273-297.
• 15. Mohammad-Rahimi H, Rokhshad R, Bencharit S, Krois J, Schwendicke F. Deep learning: a primer for dentists and dental researchers. J Dent. 2023;130:104430.
• 16. Seeman E. Sexual dimorphism in skeletal size, density, and strength. J Clin Endocrinol Metabolism. 2001; 86(10):4576-4584.
• 17. Decker SJ, Davy-Jow SL, Ford JM, Hilbelink DR. Virtual determination of sex: metric and nonmetric traits of the adult pelvis from 3D computed tomography models. J Forensic Sci. 2011;56(5):1107-1114.
• 18. Luo L, Wang M, Tian Y, et al. Automatic sex determination of skulls based on a statistical shape model. Comput Math Methods Med. 2013;2013:251628.
• 19. Radulesco T, Michel J, Mancini J, Dessi P, Adalian P. Sex estimation from human cranium: forensic and anthropological interest of maxillary sinus volumes. J Forensic Sci. 2018;63(3):805-808.
• 20. Crisco JJ, Coburn JC, Moore DC, Upal MA. Carpal bone size and scaling in men versus in women. J Hand Surg Am. 2005;30:35-42.
• 21. DeSilva R, Flavel A, Franklin D. Estimation of sex from the metric assessment of digital hand radiographs in a Western Australian population. Forensic Sci Int. 2014;244:e311-314.
• 22. Ibrahim MA, Khalifa AM, Hagras AM, Alwakid NI. Sex determination from hand dimensions and index/ring finger length ratio in North Saudi population: Medico-legal view. Egyptian J Forensic Sci. 2016;6(4):435-444.
• 23. Aboul-Hagag KE, Mohamed SA, Hilal MA, Mohamed EA. Determination of sex from hand dimensions and index/ring finger length ratio in Upper Egyptians. Egyptian J Forensic Sci. 2011;1(2):80-86.
• 24. Haq IU, Ali H, Wang HY, Lei C, Ali H. Feature fusion and ensemble learning-based CNN model for mammographic image classification. J King Saud University-Computer Information Sci. 2022;34(6):3310-3318.
• 25. Kumbasar N, Kılıç, R, Oral EA, Ozbek IY. Comparison of the spectrogram, persistence spectrum, and percentile spectrum-based image representation performances in drone detection and classification using novel HMFFNet: Hybrid Model with Feature Fusion Network. Expert Systems with Appl. 2022; 206:117654.
• 26. Adapa S, Enireddy V. Multimodal face shape detection based on human temperament with hybrid feature fusion and Inception V3 extraction model. Comp Methods Biomechanics Biomed Engineering: Imaging Visualization. 2023;15:1839-1857.
• 27. Sarić R, Kevrić J, Čustović E, Jokić D, Beganović N. Evaluation of skeletal gender and maturity for hand radiographs using deep convolutional neural networks. 6th International Conference on Control, Decision Information Technologies (CoDIT). 2019.
• 28. Bewes J, Low A, Morphett A, Pate F, Henneberg M. Artificial intelligence for sex determination of skeletal remains: application of a deep learning artificial neural network to human skulls. J Forensic Leg Med. 2019;62:40-43.
• 29. Yang W, Liu X, Wang K, Hu J, Geng G, Feng J. Sex determination of three-dimensional skull based on improved backpropagation neural network. Comput Math Methods Med. 2019; 6:1-8.
• 30. Afifi M. Gender recognition and biometric identification using a large dataset of hand images. arXivpreprintarXiv: Computer Vision and Pattern Recognition 1711.04322 2017
• 31. Darmawan MF, Yusuf SM, Rozi MA, Haron H. Hybrid PSO-ANN for sex estimation based on length of left hand bone. IEEE Student Conference on Research and Development (SCOReD). 2015;478-483.