## 4 Conclusions  
Identifying a small hairline fracture in a CT scan of a spine injury is challenging, and the risk of misclassification is higher. Our work focused on detecting the fractures in C-spine vertebrae using CT scans. It aimed to assist medical professionals in further treatment planning. Our approach utilized three CNNs as a base model and fused their prediction using a Bayesian-based probabilistic-ensemble learning approach. The objective of the proposed method was to improve the overall classification result. To accomplish this task, preprocessing steps, including handling the DICOM images and data augmentation, were performed; after that, base models were trained and fine-tuned using hyperparameter optimization and a 5-fold CV strategy. To validate the effectiveness of the proposed model, conventional ensemble techniques were also implemented with our dataset to combine the predictions from the base models, and the results were compared. Finally, it was observed that the proposed model yielded the best result in terms of accuracy, specificity, sensitivity, and F1-score. Since this work was performed on a diverse dataset obtained from various sites/modalities, it also turned out that the proposed model was more generalized, enabling potential integration with clinical use following expert radiologists' verification. The proposed method may have vast potential for multidisciplinary applications, including, but not limited to, brain tumor detection, lung disease diagnosis, plant disease detection, and histopathology image analysis. Although the proposed model showed excellent results, it also possesses some limitations. The results obtained by the proposed model are promising; however, clinical validation is a logical next step to further confirm its efficacy. Moreover, the computational complexity of our approach, although higher than that of a single model, is a worthwhile trade-off for the improved performance and robustness. Future research directions include exploring attention-based models to reduce computational costs and integrating our model into clinical workflows to enhance practical applicability. The proposed framework is accurate in distinguishing the fracture and the non-fracture vertebrae; however, the object detection models, like Faster R-CNN and Mask R-CNN, can be beneficial in localizing the fracture to identify the exact location of the injury. In addition, this study was validated on a robust dataset, where the ground truth was established by multiple experts from various countries. Although this multi-expert approach strengthens the study's foundation, it also introduces potential inter-observer variability among the radiologists. To address this, future collaborations with medical experts will be invaluable in assessing consistency and enhancing the model's generalizability. Furthermore, despite the success of ensemble learning, it is harder to visualize and interpret the features, since we cannot explicitly provide the feature activation maps obtained from the proposed method as the fusion of the results of the base learners is performed at the decision level. As CT scans provide volumetric data, performing a 2D CNN may distinguish the fractured image from non-fractured, but it is difficult to determine the extent of injury. An essential future area for study is the clinical validation of the proposed model, which is a crucial next step, offering a promising avenue for future research. In addition, to detect the fracture extent and exact slice in volumetric CT scans, integrating the spatial attention and channel attention module (CAM) in 3D CNN may help to identify the exact fracture region more efficiently by assigning a higher weight to the injured area during training. Furthermore, the combination of spatial attention and CAM may be more effective as spatial attention focuses more on the region of interest (fractured area), whereas CAM highlights the channel that carries the most important features. **Author Contributions:** Conceptualization, A.K.P. and K.S. ; methodology, A.K.P.