Classification of Indonesian adult forensic gender using cephalometric radiography with VGG16 and VGG19: a Preliminary research

Authors

  • Vitria Wuri Handayani Doctoral Program of Medical Science, Medical Faculty, Universitas Airlangga, Surabaya, Indonesia; Nursing Department, Pontianak Polytechnic Health Ministry, Pontianak, Indonesia https://orcid.org/0000-0002-5076-0118
  • Ahmad Yudianto Department of Forensics and Medicolegal, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia; Magister of Forensic Sciences, Postgraduate School, Universitas Airlangga, Surabaya https://orcid.org/0000-0003-4754-768X
  • Mieke Sylvia M.A.R Magister of Forensic Sciences, Postgraduate School, Universitas Airlangga, Surabaya; Forensic Odontology Department, Dental Medical Faculty, Univesitas Airlangga, Surabaya, Indonesia https://orcid.org/0000-0001-8821-0157
  • Riries Rulaningtyas Physics Department, Sains and Technology Faculty, Universitas Airlangga, Surabaya, Indonesia; Biomedical Department, Sains and Technology Faculty, Universitas Airlangga, Surabaya, Indonesia https://orcid.org/0000-0001-7058-1566
  • Muhammad Rasyad Caesarardhi Department of Information Systems, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia https://orcid.org/0000-0002-1235-8849

DOI:

https://doi.org/10.2340/aos.v83.40476

Keywords:

cephalometry, gender determination, VGG16, VGG19

Abstract

Background: The use of cephalometric pictures in dental radiology is widely acknowledged as a dependable technique for determining the gender of an individual. The Visual Geometry Group 16 (VGG16) and Visual Geometry Group 19 (VGG19) algorithms have been proven to be effective in image classification.

Objectives: To acknowledge the importance of comprehending the complex procedures associated with the generation and adjustment of inputs in order to obtain precise outcomes using the VGG16 and VGG19 algorithms.

Material and Method: The current work utilised a dataset including 274 cephalometric radiographic pictures of adult Indonesians’ oral health records to construct a gender classification model using the VGG16 and VGG19 architectures using Python.

Result: The VGG16 model has a gender identification accuracy of 93% for females and 73% for males, resulting in an average accuracy of 89% across both genders. In the context of gender identification, the VGG19 model has been found to achieve an accuracy of 0.95% for females and 0.80% for men, resulting in an overall accuracy of 0.93% when considering both genders.

Conclusion: The application of VGG16 and VGG19 models has played a significant role in identifying gender based on the study of cephalometric radiography. This application has demonstrated the exceptional effectiveness of both models in accurately predicting the gender of Indonesian adults.

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Published

2024-05-21