Deep Recurrent Neural Network-Based Assessment of Human Dental Age and Gender from Dental Radiographs

Abstract

The First Chapter's introduction discussed the ability to determine a person's gender and dental age with great accuracy and efficiency is made possible by this technology. It has also done a study that aims to leverage the groundbreaking advantages of deep learning in the dental age and gender evaluation by providing an accurate and automated approach that goes beyond the constraints of traditional methods. The Second Chapter's Literature Review explained Deep Learning Applications in Dental Radiography and Traditional Methods for Dental Age and Gender Assessment and Datasets and Annotations for Dental Radiographs. It has also done Temporal Dependencies in Dental Radiographs. The Third Chapter Methodology discussed that dental radiography data contains rich environmental information that necessitates a nuanced comprehension; this study employs an interpretivist research philosophy. It has also been done to examine pre-existing ideas and models in the context of tooth age and gender evaluation, a deductive approach is used in this study.