## 3 Results
### End-to-End Pipeline  
The results presented in this section originate from the sequential pipeline, which integrates the best-performing detection and classification models, following the methodology detailed in Section 2.4. Figure 2a is populated version of the descriptive matrix provided in Table 3. This matrix gives a complete overview of the results of our pipeline in the testing \begin{table}
\begin{tabular}{l l} \hline \hline
**Search Type** & **Hyperparameter Results** \\ \hline
**Grid Search** & \\ Number of Selected Features & 15 \\ Proportion of Excluded Slices & 80\% \\ Number of Cross-Validation Folds & 10 \\ \hline
**Random Search** & \\ XGBoost—Number of Estimators & 50 \\ XGBoost—Learning Rate & 0.01 \\ Random Forest-Number of Estimators & 200 \\ Logistic Regression—Regularization (\(C\)) & 1.0 \\ \hline \hline \end{tabular} Note: Bold text indicates category headers used to group related parameters.  
\end{table} 
Table 7: Best parameters for the classification model, results from random search and grid search. \begin{table}
\begin{tabular}{l l} \hline \hline
**Metric** & **Value (\%)** \\ \hline Accuracy & 88.97 \\ Precision & 90.31 \\ Recall (Sensitivity) & 94.20 \\ Specificity & 77.13 \\ F1-Score & 92.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluation metrics for the classification model on the testing set—non-adenoma vs. adenoma. dataset. Additionally, Figure 2b focuses specifically on the detection and classification outcomes for adrenal adenomas, which represent the primary focus of this study. These matrices lay the foundation for calculating the metrics necessary to evaluate the overall quality of the machine learning pipeline. Table 9 presents the computed metrics derived from both the overall confusion matrix (Figure 2a) and the adenoma-specific confusion matrix (Figure 2b). The pipeline achieved an overall accuracy of 85.06%, indicating a high proportion of correct predictions across all classes. The macro metrics, which treat each class equally, further highlight the robustness of the pipeline, with a macro precision of 86.27%, a macro \begin{table}
\begin{tabular}{l l} \hline \hline
**Metric Category** & **Value (\%)** \\ \hline
**Overall Metrics** & \\ Accuracy & 85.06 \\ Macro Precision & 86.27 \\ Macro Recall (Sensitivity) & 97.62 \\ Macro F1-Score & 90.13 \\ Macro Specificity & 92.00 \\ Weighted Precision & 98.14 \\ Weighted Recall (Sensitivity) & 96.84 \\ Weighted F1-Score & 97.21 \\ Weighted Specificity & 90.93 \\ \hline
**Adenoma-Specific Metrics** & \\ Accuracy & 87.45 \\ Precision & 82.22 \\ Recall & 87.63 \\ F1-Score & 84.84 \\ Specificity & 87.33 \\ \hline \hline \end{tabular} Note: Bold text indicates category headers used to group related metrics.  
\end{table} 
Table 9: Overall and adenoma-specific metrics. Figure 2: Confusion matrices. (**a**) Overall end-to-end confusion matrix. (**b**) Adenoma-specific end-to-end confusion matrix. recall of 97.62%, and a macro F1-score of 90.13%. However, the weighted metrics, which account for class imbalances, showcased significantly higher values for precision (98.14%) and F1-score (97.21%). This gap between macro and weighted metrics suggests that the model performs exceptionally well on the adenoma and non-lesion classes (the majority classes) while facing challenges with the less frequent non-adenoma class. Specifically, the increased weighted precision and F1-score underscore difficulties in accurately classifying non-adenoma lesions, likely due to their lower representation in the dataset and overlapping imaging characteristics with adenomas. For adenoma-specific results, the pipeline demonstrated an accuracy of 87.45%, reflecting its effectiveness in handling this class. The precision of 82.22% and recall of 87.63% indicate that the model maintains a good balance between minimizing false positives and correctly identifying adenomas. The F1-score of 84.84% highlights the overall balance between precision and recall.