  Cervical spine fractures constitute a critical medical emergency, with the
potential for lifelong paralysis or even fatality if left untreated or
undetected. Over time, these fractures can deteriorate without intervention. To
address the lack of research on the practical application of deep learning
techniques for the detection of spine fractures, this study leverages a dataset
containing both cervical spine fractures and non-fractured computed tomography
images. This paper introduces a two-stage pipeline designed to identify the
presence of cervical vertebrae in each image slice and pinpoint the location of
fractures. In the first stage, a multi-input network, incorporating image and
image metadata, is trained. This network is based on the Global Context Vision
Transformer, and its performance is benchmarked against popular deep learning
image classification model. In the second stage, a YOLOv8 model is trained to
detect fractures within the images, and its effectiveness is compared to
YOLOv5. The obtained results indicate that the proposed algorithm significantly
reduces the workload of radiologists and enhances the accuracy of fracture
detection.
