## 3 Results
### Lesion Classification  
The lesion classification model was trained using all available MRI sequences. This decision aligns with the best training approach for the detection model, which also utilized the same MRI sequences. To select the best adrenal lesion classification model, we conducted an extensive search for global parameters using a grid search and a random search for internal parameters of the individual base learners. Table 7 summarizes the hyperparameter results from both search strategies. As a result, the optimal model incorporated 15 radiomic features selected through LASSO regression and PCA, comprising  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Metric Category** & **Value (\%)** \\ \hline
**Detection-Specific Metrics** & \\ Average Precision (AP) & 42.68 \\ AP at 50\% IoU (AP50) & 85.80 \\ \hline
**Classification Metrics—Non-Lesion vs. Lesion—IoU: 50\%** & \\ Accuracy & 88.23 \\ Precision & 85.85 \\ Recall (Sensitivity) & 89.47 \\ Specificity & 87.14 \\ F1-Score & 87.62 \\ \hline \hline \end{tabular} Note: Bold text indicates category headers used to related metrics.  
\end{table}
Table 6: Evaluation metrics for the best lesion detection model on the testing set.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hyperparameter** & **Value** \\ \hline Learning Rate & 0.0001 \\ Backbone & ResNet-50 \\ Backbone freeze at & 2 \\ Focal Loss Alpha (a) & 0.25 \\ Focal Loss Gamma (\(\gamma\)) & 2.0 \\ Detection Score Threshold & 0.5 \\ Non-Maximum Suppression (NMS) Threshold & 0.6 \\ Images per Batch & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Best model hyperparameters for lesion detection.  
first-order features (Energy, Kurtosis, Skewness), second-order features (GLRLM, GLSZM), and higher-order features (GLDM). The training set consisted of the most representative 20% of lesion slices, emphasizing slices that best captured the characteristics of adrenal lesions, i.e., closer to the center of the lesion. Cross-validation with 10 folds was employed, using recall as the evaluation metric to prioritize sensitivity in the model's predictions.  
This optimization process culminated in a robust model configuration, whose performance is detailed in the subsequent results, detailed in Table 8. These results were obtained in the complete testing set without any slice exclusion.  
The classification model achieved strong performance in distinguishing adrenal adenomas from non-adenoma lesions, as indicated by its evaluation metrics. With an accuracy of 88.97%, the model demonstrated high overall correctness in its predictions. The precision of 90.31% reflects a low false adenoma prediction rate. The recall (sensitivity) of 94.20% highlights the model's ability to detect adenomas effectively, minimizing the number of missed adenomas. However, the specificity of 77.13% indicates a relatively higher false positive rate when identifying non-adenomas, suggesting that some non-adenoma lesions were misclassified as adenomas. These results suggest that the classification model is highly effective in identifying adrenal adenomas, although improvements could be made to enhance the classification of non-adenomas.