  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.
