GENDER IDENTIFICATION FROM LEFT HAND-WRIST X-RAY IMAGES WITH A HYBRID DEEP LEARNING METHOD

Abstract

In forensic investigations, characteristics such as gender, age, ethnic origin, and height are important in determining biological identity. In this study, we developed a deep learning-based decision support system for gender recognition from wrist radiographs using 13,935 images collected from individuals aged between 2 and 79 years. Differences in all regions of the images, such as carpal bones, radius, ulna bones, epiphysis, cortex, and medulla, were utilized. A hybrid model was proposed for gender determination from X-ray images, in which deep metrics were combined in appropriate layers of transfer learning methods. Although gender determination from X-ray images obtained from different countries has been reported in the literature, no such study has been conducted in Turkey. It was found that gender discrimination yielded different results for males and females. Gender identification was found to be more successful in females aged between 10 and 40 years than in males. However, for age ranges of 2-10 and 40-79 years, gender discrimination was found to be more successful in males. Finally, heat maps of the regions focused on by the proposed model were obtained from the images, and it was found that the areas of focus for gender discrimination were different between males and females.