Deep learning models for predicting the position of the head on an X-ray image for Cephalometric analysis

Abstract

Cephalometric analysis is used to identify problems in the development of the skull, evaluate their treatment, and plan for possible surgical interventions. The paper aims to develop a Convolutional Neural Network that will analyze the head position on an X-ray image. It takes place in such a way that it recognizes whether the image is suitable and, if not, suggests a change in the position of the head for correction. This paper addresses the exact rotation of the head with a change in the range of a few degrees of rotation. The objective is to predict the correct head position to take an X-ray image for further Cephalometric analysis. The changes in the degree of rotations were categorized into 5 classes. Deep learning models predict the correct head position for Cephalometric analysis. An X-ray image dataset on the head is generated using CT scan images. The generated images are categorized into 5 classes based on a few degrees of rotations. A set of four deep-learning models were then used to generate the generated X-Ray images for analysis. This research work makes use of four CNN-based networks. These networks are trained on a dataset to predict the accurate head position on generated X-Ray images for analysis. Two networks of VGG-Net, one is the U-Net and the last is of the ResNet type. The experimental analysis ascertains that VGG-4 outperformed the VGG-3, U-Net, and ResNet in estimating the head position to take an X-ray on a test dataset with a measured accuracy of 98%. It is due to the incorrectly classified images are classified that are directly adjacent to the correct ones at intervals and the misclassification rate is significantly reduced.