## 2 Materials and Methods
### Base Models' Training and Their Hyperparameter Optimization

Table 1: Experimental setup and chosen hyperparameters for base models. where \(y_{i}\) and \(\hat{y}_{i}\) represent the true and predicted value, respectively, and \(\alpha_{j}\) represent the weights for the class \(j=0,1\), which are obtained using the formula given below,  
\[\alpha_{j}=\frac{N}{2n_{j}}\]  
Here, \(N\) is the total number of samples, and \(n_{j}\) denotes the total number of samples in class \(j\). The 5-fold CV also validates and ensures that the base models are not biased to any particular fold of training data. The statistical insights of the base learners using 5-fold CV are displayed in Figure 6 and their performance on the validation set is presented in Table 2. From the box plots of each base model, it is evident that all four performance metrics vary in a narrow range, whose values are also presented in Table 2 in the form of mean \(\pm\) SD. To gain insights into the decision-making process of our base models and to understand which feature of the CT image contributes more for making predictions, this study \begin{table}
\begin{tabular}{l l c c} \hline \hline
**Model** & **Metric** & **Training** & **Validation** \\ \hline \multirow{6}{*}{WRN-40} & _Acc_ & \(88.29\pm 2.18\) & \(84.3\pm 2.61\) \\  & _Spec_ & \(88.1\pm 3.64\) & \(85.2\pm 3.19\) \\  & _Sens_ & \(86.12\pm 2.5\) & \(80.1\pm 3.09\) \\  & _F1_ & \(86.4\pm 2.31\) & \(82.3\pm 3.03\) \\ \hline \multirow{6}{*}{DenseNet-121} & _Acc_ & \(89.17\pm 1.54\) & \(86.2\pm 3.34\) \\  & _Spec_ & \(87.4\pm 2.82\) & \(82.6\pm 3.73\) \\ \cline{1-1}  & _Sens_ & \(91.3\pm 2.72\) & \(89.6\pm 2.53\) \\ \cline{1-1}  & _F1_ & \(89.32\pm 3.14\) & \(86\pm 3.82\) \\ \hline \multirow{6}{*}{EfficientNet-B7} & _Acc_ & \(94.17\pm 2.17\) & \(90.26\pm 1.78\) \\  & _Spec_ & \(92.7\pm 2.93\) & \(88.14\pm 1.54\) \\ \cline{1-1}  & _Sens_ & \(93.24\pm 2.03\) & \(91.8\pm 2.08\) \\ \cline{1-1}  & _F1_ & \(93.8\pm 2.15\) & \(89.8\pm 2.07\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance metrics across 5-Fold CV for base learners. Figure 6: Box plots of performance metrics of base models of 5-fold CV. (**a**) WRN-40; (**b**) DenseNet-121; and (**c**) EfficientNet-B7. utilized the SHapley Additive exPlanations (SHAP) technique [34], which is demonstrated in Figure 7. The SHAP values, represented by color gradients ranging from dark blue (highest negative contribution) to dark pink (highest positive contribution) via white (no contribution), illustrate the contribution of each feature to the model's prediction. Furthermore, a small fracture area highlighted in a green rectangular box is zoomed in and displayed in the adjacent column for enhanced visualization. The first, second, and third rows display the SHAP results obtained from WRN-40, DenseNet-121, and EfficientNet-B7, respectively. It can also be observed that, for a test image belonging to a fracture class, the output in the non-fracture SHAP explanation column is obtained with blue, indicating false prediction, while the fracture SHAP explanation column is coded with pink, indicating correct prediction. Conversely, when the test image belongs to the non-fracture class, the color-coding is as follows: the non-fracture SHAP explanation column is colored with pink, indicating correct prediction, and the blue will appear in the fracture SHAP explanation column, suggesting false prediction. Feature maps obtained from the second convolutional layer of the base models and the class activation maps (CAM) from their last convolutional layers are displayed in Figure 8. The leftmost image in Figure 8 represents a randomly chosen fractured-class image from the test set, which is used to visualize how well the base learners make predictions on unseen data. The light green arrows in the chosen sample image indicate two fractured regions.