## 1 Introduction  
The spine consists of a stack of several bones of different shapes known as vertebrae. The uppermost seven vertebrae, ranging from C1 to C7 of the spine are called the cervical spine (C-spine). The vertebrae protect the spinal cord from injury and also support the stability of the entire body [1]. Any serious vertebral injury might lead to spinal cord injury (SCI), and that might have severe consequences [2]. A recent study reported that, out of the total SCI diagnosed each year, 55% belonged to C-spine injury (CSI) [3]. Any vertebral injury may lead to life-threatening conditions due to dysfunction of the nervous system [4]. The cause of vertebral fracture (VF) may be traumatic or non-traumatic. The major traumatic causes include vehicular accidents, falls,gunshot injuries, herniated discs, heavy lifting, and sudden movements that strain the back whereas, the non-traumatic causes are osteoporosis, vascular events, and degenerative disc disease [5]. The initial diagnosis of VF is primarily conducted using a CT scan, and a well-trained radiologist is required to distinguish between a fracture image and a non-fracture image. However, it often creates challenges to the radiologist due to the low spatial resolution of CT images which is the consequence of low radiation dose during the image acquisition. Recent advancements in computing power have enabled the development of software-based methods for iterative image reconstruction in CT, enabling simultaneous reduction of noise and improvement of overall image quality. Additional details on iterative methods are found in [6; 7; 8; 9]. The incidence of spinal trauma and subsequent VFs is steadily increasing day by day [10]. Fast diagnosis of VF can significantly accelerate treatment planning and save millions of lives [11]. The manual diagnosis of CSI, particularly the subtle fractures, can be challenging and time-consuming due to the noise, homogeneity between fracture and non-fracture regions, and low-level features in CT images. Small fractures are not immediately apparent on initial examination; however, they still can cause significant pain and may lead to other complications if not treated in a timely way. Artificial intelligence (AI) has significantly contributed in medical imaging, supporting various applications like lesion detection, disease classification, quantification, and segmentation [12]. Some previous work on the application of AI in spine injury and recent advancements of deep learning in this area are discussed below. In a study by [PERSON] et al. [13], CSI was detected using Faster-RCNN, where VGG-16 and ResNet-50 were used for feature extraction, achieving 72.3% and 88.6% mean average precision (mAP), respectively. However, the study was conducted on a dataset obtained from a single modality in a hospital, which hindered the generalizability of the model. In another study, C-spine fractures were detected using a convolutional neural network (CNN) developed by _Aidoc_. However, the model failed to detect acute fracture-dislocation, and any injuries in the lower C-spine [14]. In another work [15], vertebral compression fracture (VCF) was detected using mask R-CNN in which ResNet-101 was utilized for feature extraction, followed by a region proposal network to capture the affected area. The accuracy of their model was merely 80.4%. In [16], an ensemble learning approach, majority voting, was utilized to detect the VCF in the thoracic spine. However manual annotation was performed for all the images, which was time-consuming, and an accuracy of 86.67% was achieved. In a study conducted by [PERSON] et al. [17], an AI-based VCFs detection model was developed using X-ray images. The basic and shallow ResNet-18 was used as a base model which managed to achieve a low accuracy of 82.9%. A deep CNN was developed for VF detection using plain abdominal radiographs, achieving accuracy, specificity, sensitivity, and AUC of 73.59%, 73.02%, 73.81%, and 72%, respectively.