Classification of cervical vertebral morphology using deep learning

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Abstract
Purpose: This study investigated whether or not cervical vertebral morphology could be automatically classified using a deep-learning system.Methods: Nine hundred and ninety-nine cephalometric radiographs obtained for orthodontic diagnoses were used in the present study. Segmented rectangular images between the second and fourth cervical vertebrae were resized with a patch size of 300 × 300 pixels (resized images) or padded with black images on the right and left sides, keeping their aspect ratio unchanged (padded images). Seven hundred and ninety-nine and 200 images were defined as the training and test data, respectively. Le Net and Alex Net Neural Network Console architectures were used to classify the images into two groups based on cervical vertebral maturation (CVM). The classifications of resized images using Le Net and Alex Net were designated Le Net Resizing and Alex Net Resizing, respectively. The classifications of the padded images using Le Net and Alex Net were designated Le Net Padding and Alex Net Padding, respectively. The accuracy of the classification using deep learning was evaluated.Results: The average accuracies of the classification of CVM using Le Net Resizing and Le Net Padding were 59.0% and 89.5%, respectively. The average accuracy of classification of CVM using Alex Net Resizing and Alex Net Padding was 90.5% and 93.0%, respectively.Conclusion: The deep-learning system using Le Net Padding, Alex Net Resizing, and Alex Net Padding showed high accuracy in classifying images into two groups based on CVM.

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