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Deep learning models for predicting the position of the head on an X-ray image for Cephalometric analysis.
- Source :
-
Intelligent Data Analysis . 2023 Supplement 1, Vol. 27, p3-27. 25p. - Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *X-ray imaging
*DEEP learning
*CONVOLUTIONAL neural networks
*IMAGE analysis
Subjects
Details
- Language :
- English
- ISSN :
- 1088467X
- Volume :
- 27
- Database :
- Academic Search Index
- Journal :
- Intelligent Data Analysis
- Publication Type :
- Academic Journal
- Accession number :
- 173929478
- Full Text :
- https://doi.org/10.3233/IDA-237430