Human Gender Prediction Based on Deep Transfer Learning from Panoramic Radiograph Images

TL;DR

This study employs deep transfer learning with DenseNet121 to accurately determine gender from panoramic radiograph images, achieving over 97% success rate, offering a faster and more reliable alternative to manual methods.

Contribution

Introduces a deep transfer learning approach using DenseNet121 for gender prediction from panoramic radiographs, outperforming other models in accuracy.

Findings

Achieved 97.25% accuracy in gender classification.

DenseNet121 outperformed VGG16, ResNet50, and EfficientNetB6.

Transfer learning improved training efficiency and performance.

Abstract

Panoramic Dental Radiography (PDR) image processing is one of the most extensively used manual methods for gender determination in forensic medicine. With the assistance of the PDR images, a person's biological gender determination can be performed through analyzing skeletal structures expressing sexual dimorphism. Manual approaches require a wide range of mandibular parameter measurements in metric units. Besides being time-consuming, these methods also necessitate the employment of experienced professionals. In this context, deep learning models are widely utilized in the auto-analysis of radiological images nowadays, owing to their high processing speed, accuracy, and stability. In our study, a data set consisting of 24,000 dental panoramic images was prepared for binary classification, and the transfer learning method was used to accelerate the training and increase the performance of our proposed DenseNet121 deep learning model. With the transfer learning method, instead of starting the learning process from scratch, the existing patterns learned beforehand were used. Extensive comparisons were made using deep transfer learning (DTL) models VGG16, ResNet50, and EfficientNetB6 to assess the classification performance of the proposed model in PDR images. According to the findings of the comparative analysis, the proposed model outperformed the other approaches by achieving a success rate of 97.25% in gender classification.