Gender classification on digital dental x-ray images using deep convolutional neural network

摘要

Human teeth are more durable in nature and tend to survive even after the death of a person. For this reason, teeth play a momentous role in the forensic department to identify a missing or dead person. Gender discrimination is also a key process in forensic analysis. However, designing an automated dental gender estimation based on deep learning techniques still needs more research efforts. This paper proposed an algorithm to estimate the gender of a human from dental x-ray image (DXI). It consists of three steps namely pre-processing, segmentation, and gender classification. In the initial step, the DXI is denoised to remove the unwanted noises such as Gaussian, speckle, and impulse using a prime magic square filter. In the second step, the gradient-based recursive thresholding segmentation is used to segment the denoised DXI. At the final stage, the image is classified using the Resnet50 deep convolutional neural network classification algorithm. The database of 1000 dental images has taken and divided into 600 training samples and 400 testing samples during experimental evaluation. Various performance measures of the proposed segmentation and classification method are evaluated. The proposed system has achieved 98.27% of classification accuracy. Moreover, the performance of the proposed work is compared with similar existing methods. It shows that the proposed system produced better results. Therefore, the proposed gender classification system supports the dentist in forensic applications to record the dental post mortem details in case of a big disaster.