## 3 Results
### Tumoral vs. Non-Tumoral Classification
A DeLong test was performed, considering AUC scores and 95% IC reported in Table 2. The results of this test, obtained considering the optimal CNN + SVM algorithm as a reference, are reported in Table 3.  
Figure 3 shows four bar charts illustrating MLcps metric values for different classifiers--KNN, RF, GB, SVM, and Majority Vote--applied to four deep learning architectures: Xception, DenseNet121, ResNet50, and VGG19. In the Xception-based architecture, SVM achieves the highest score of 0.79, followed closely by RF at 0.78, while Majority Vote reaches 0.77. The DenseNet121-based architecture exhibits a strong performance across all classifiers, with SVM obtaining the highest value at 0.86 and Majority Vote at 0.84. ResNet50 demonstrates high classification effectiveness, with SVM leading at 0.88 and Majority Vote following at 0.82. VGG19-based architecture achieves the highest overall results, with SVM \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**ML Algorithm**} \\ \hline
**Metric** & **Modified CNN** & **KNN** & **RF** & **GB** & **SVM** & **Majority Voting * \\ \hline \multirow{4}{*}{AUC + 95\% IC} & ResNet50 & 0.70 [0.56, 0.84] & 0.84 [0.72, 0.94] & 0.89 [0.79, 0.96] & 0.92 [0.84, 0.99] & 0.81 [0.69, 0.93] \\  & VGG19 & 0.79 [0.66, 0.90] & 0.83 [0.70, 0.94] & 0.86 [0.75, 0.96] & 0.97 [0.92, 1.00] & 0.82 [0.70, 0.93] \\  & DenseNet121 & 0.78 [0.65, 0.90] & 0.86 [0.74, 0.96] & 0.87 [0.76, 0.96] & 0.89 [0.73, 1.00] & 0.78 [0.65, 0.90] \\  & Xception & 0.77 [0.64, 0.89] & 0.82 [0.70, 0.92] & 0.81 [0.69, 0.92] & 0.85 [0.68, 1.00] & 0.74 [0.60, 0.88] \\ \hline \multirow{4}{*}{F1-score} & ResNet50 & 0.78 & 0.85 & 0.88 & 0.89 & 0.87 \\  & VGG19 & 0.82 & 0.86 & 0.88 & 0.97 & 0.89 \\  & DenseNet121 & 0.83 & 0.88 & 0.87 & 0.94 & 0.85 \\  & Xception & 0.81 & 0.85 & 0.84 & 0.92 & 0.81 \\ \hline \multirow{4}{*}{Sensitivity} & ResNet50 & 0.72 & 0.92 & 0.92 & 1.00 & 0.92 \\  & VGG19 & 0.72 & 0.88 & 0.92 & 1.00 & 1.00 \\  & DenseNet121 & 0.88 & 0.92 & 0.92 & 1.00 & 0.92 \\  & Xception & 0.76 & 0.88 & 0.76 & 1.00 & 0.84 \\ \hline \multirow{4}{*}{Specificity} & ResNet50 & 0.65 & 0.47 & 0.59 & 0.70 & 0.65 \\  & VGG19 & 0.71 & 0.65 & 0.59 & 0.80 & 0.71 \\ \cline{1-1}  & DenseNet121 & 0.65 & 0.59 & 0.65 & 0.60 & 0.65 \\ \cline{1-1}  & Xception & 0.71 & 0.59 & 0.59 & 0.40 & 0.65 \\ \hline \hline \end{tabular}  
* Based on KNN, RF, and GB.  
\end{table} 
Table 2: Performance metrics of the 20 hybrid/approaches predicting tumoral versus non-tumoral classifications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**ML Algorithm**} \\ \hline
**Modified CNN** & **KNN** & **RF** & **GB** & **SVM** & **Majority Voting * \\ \hline ResNet50 & 0.007 & 0.24 & 0.60 & - & 0.13 \\ VGG19 & 0.005 & 0.03 & 0.06 & - & 0.02 \\ DenseNet121 & 0.24 & 0.74 & 0.82 & - & 0.24 \\ Xception & 0.44 & 0.76 & 0.69 & - & 0.31 \\ \hline \hline \end{tabular}  
* Based on KNN, RF, and GB. The symbol ”-” indicates the reference model for the DeLong test comparison.  
\end{table} 
Table 3: \(p\)-value (DeLong test) comparing hybrid models distinguishing tumoral (i.e., EAOC or NEOC) vs. non-tumoral ovary classification versus each CNN + SVM approach, assumed as a reference. reaching 0.93, making it the best-performing classifier, while Majority Vote records 0.87. These results highlight SVM and Majority Vote as the most effective classification strategies across architectures. Figure 4 shows the confusion matrices for the different ML algorithms combined with different CNN architectures compared with the majority voting ensemble, too. Majority voting generally outperforms individual models, especially in reducing non-tumoral ovary misclassification. SVM shows strong classification performance, particularly for the tumoral ovary class, while GB and RF have variable results depending on the CNN used. These findings reinforce the advantage of ensemble learning in improving robustness and reducing model-specific biases.