## 1 Introduction
While it demonstrated fair performance, it was solely trained on plain radiographs [18]. In [19], [PERSON] et al. proposed a method for VCF detection and obtained 74% accuracy, 68% specificity, and 80% sensitivity with an AUC of 89%. Despite moderate performance, both models lack robustness and generalization, as they worked on a small dataset obtained from a single modality. In another study [20], a model was proposed to detect osteoporotic VF and utilized the fusion of three CNNs, achieving accuracy, specificity, sensitivity, and AUC of 89%, 92%, 83%, and 87.6%, respectively. However, the model was trained on only the bone part of the vertebral images, obtained using the YOLOv5 object detection model, indicating that it will not be useful for real-time applications as clinical images contain bone structure, soft tissue,and other background parts. Furthermore, their ensemble approach was not made explicit, so systematically combining predictions from CNNs may be more useful in improving the result. A CNN-based model for VF detection using CT images was developed by [PERSON] et al. [21], achieving 93% accuracy, 93% specificity, 94% sensitivity, and an AUC of 94%. While the model shows promising results, the study was conducted on a small dataset, which may restrict the generalizability. A generative adversarial network (GAN)-assisted CNN was developed to detect VF, which improved classification accuracy from 73% to 89% and achieved an F1-score of 90%. However, more synthetic images might distort the model's learning capabilities, and make it less reliable for real-world application. Handling the uncertainties about the model's prediction would be beneficial in this case [22]. [PERSON] et al. [23] proposed a method for VCF detection using CT scans and achieved 92% accuracy, 91.7% specificity, and 92.3% sensitivity. While it has impressive performance, the model focuses primarily on patients above 50 years who have undergone chest-abdominal-pelvis CT scans obtained from a single modality from a hospital, which suggests the model might not be robust. Although AI has made significant contributions in various areas pertinent to the medical field, its use, specifically, in detecting C-spine vertebral fractures, is in the nascent stage. AI-assisted methods could be useful in the early detection of vertebral injuries for effective treatment and management in such cases. A recent survey found that a few studies had been undertaken on AI-assisted vertebral fracture detection by 2022 [24]. The present study aims to design and evaluate an AI-based model to detect fractures in C-spine vertebrae using CT images. The major contributions of this work are summarized as follows:  
* Training and fine-tuning of three CNNs with 5-fold cross-validation (CV) for VF detection using C-spine CT images and employing them as base learners. Additionally, a base model interpretability analysis is conducted using SHapley Additive exPlanations and the Grad-CAM method. * To improve the classification performance, a novel ensemble learning approach based on Bayesian probability is proposed. * To validate the potential of the proposed framework, conventional ensemble learning methods are implemented on our dataset, and then their performance is analyzed and compared with the proposed model in terms of accuracy, specificity, sensitivity, and the F1-score. The rest of the paper is structured as follows: The data preprocessing, base model training with hyperparameter optimization, and proposed methodology are explained in Section 2. The comparative analysis of results of the proposed model with base models and other traditional ensemble learning methods is reported in Section 3. The conclusions, limitations, and future scope of this study are discussed in Section 4.