## 3 Results
### Tumoral vs. Non-Tumoral Classification  
The hybrid models rely on four CNN base models and four ML meta-learners to distinguish tumoral (EAOC or NEOC) vs. non tumoral tissues (i.e., control group). Table S1 in the Supplementary Materials reports the dropout rates and L1 penalization values used for fine-tuning the final layers of four different deep learning architectures: ResNet50, VGG19, Xception, and DenseNet121. Each architecture was tested with various dropout rates (ranging from 0.0 to 0.8) and L1 penalization values (ranging from 0.0 to 1.0). The values in bold indicate the specific configurations selected for training. The dropout rate helps in preventing overfitting by randomly deactivating neurons during training, while L1 penalization is a regularization technique that encourages sparsity in model weights. This table provides insights into the optimization parameters used in the model's fine-tuning process, which aims to enhance performance and generalization. The selected values in bold reflect the best-performing configurations for training each architecture. After training and validating all the models, they were tested on the same test dataset, made up of 42 ovaries (17 non-tumoral and 25 tumoral). After the 16 models were developed (i.e., CNN coupled with KNN, RF, GB, and SVM), we further implemented four majority voting systems (based on four CNN architectures combined with the KNN, RF, and GB algorithms). Thus, we compared their performances in terms of AUC score and 95% interval confidence (IC), F1-score, sensitivity, and specificity with performances obtained with common and robust approaches in the literature, such as the SVM algorithm and majority voting ensemble. Table 2 reports the values of each metric used to evaluate each model's performance on the test set. The table compares the performance of different CNN architectures (ResNet50, VGG19, DenseNet121, and Xception) integrated with various ML algorithms (KNN, RF, GB, SVM, and majority voting) across four key metrics: AUC, F1-score, sensitivity, and specificity. Focusing on AUC, SVM achieved the highest AUC across all CNNs, with the best result for VGG19 (0.97), and strong performance for DenseNet121 and Xception (both 0.89). Regarding F1-score, SVM also outperformed other models, with VGG19 achieving the highest F1-score (0.97), followed by DenseNet121 (0.94). \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Classification for**} & \multicolumn{1}{c}{**Histopathological Classification**} & \multicolumn{1}{c}{**N\({}^{\circ}\) of VOIs**} \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Control \\ (non-tumoral) group \\ \end{tabular} } & Healthy ovaries (HO) & 46 \\ \cline{2-3}  & \multicolumn{1}{c}{Ovaries with BRCA mutation} & 13 \\ \cline{2-3}  & \multicolumn{1}{c}{Ovaries with endometriosis} & 25 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Tumoral VOIs \\ \end{tabular} } & Non-endometriosis associated ovarian cancer (NEOC) & 95 \\ \cline{2-3}  & Endometriosis-related ovarian cancer (EAOC) & 59 \\ \hline \multicolumn{2}{c}{Total delineated VOIs} & 238 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Table reporting the VOI classification used in this work and according to histopathology. In terms of sensitivity, SVM consistently reached a 1.00 sensitivity across all architectures, making it the best choice for identifying positive cases, while in terms of specificity, VGG19 with SVM had the highest specificity (0.80), ensuring reliable negative classifications. Overall, VGG19 with SVM appears to be the optimal combination, providing the highest AUC, F1-score, sensitivity, and strong specificity. This suggests it is the most balanced model, excelling in both positive and negative classification performance when the task is distinguishing tumoral vs. non tumoral tissues using CECT.