## 3 Results
### EAOC vs. NEOC Classification  
Twelve hybrid models were developed to classify EAOC vs. NEOC tissues. The models were evaluated in a test set comprising 30 ovaries (20 NEOC and 10 EAOC). Table S2 in the Supplementary Materials reports examples of dropout rate and L1 penalization obtained during fine-tuning for identifying EAOC vs. NEOC. Also in this case, the CNN approaches combined with the three investigated ML algorithms were compared with other ML approaches common in the literature, e.g., SVM algorithm and majority voting strategy. Table 4 summarizes the values of each metric used to evaluate the performance of each hybrid model on the test set, classifying EAOC vs. NEOC. The table presents the performance of different CNN architectures (ResNet50, VGG19, DenseNet121, and Xception) combined with various machine learning (ML) algorithms (KNN, RF, GB, SVM, and majority voting) across four evaluation metrics: AUC, F1-score, sensitivity, and specificity. Regarding AUC, VGG19 combined with KNN, RF, and GB achieved the highest AUC (0.86), indicating a strong discriminative ability. Based on F1-score, VGG19 with SVM (0.85) and ResNet50 with GB (0.82) showed the best balance between precision and recall. In terms of sensitivity, DenseNet121 with KNN (0.95) and RF (0.90) demonstrated the highest sensitivity, making it effective in identifying positive cases; while in terms of specificity, the majority voting with DenseNet121 (1.00) exhibited the best specificity, ensuring reliable negative predictions. Overall, VGG19 with SVM appears optimal, offering a strong AUC, F1-score, and sensitivity while maintaining good specificity using as input CE-CT images. Majority voting also achieved high specificity, which may be valuable depending on the classification priority. Furthermore, Table 5 reports the DeLong test values using the AUC and 95% IC of the SVM algorithm as a reference. Figure 5 presents the MLcps metric values of the 20 hybrid models grouped based on the CNN architecture. More in detail, Figure 5 illustrates four bar charts comparing MLcps metric values for different classifiers--KNN, RF, GB, SVM, and Majority Vote--across \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Metric**}} & \multicolumn{6}{c}{**ML Algorithm**} \\ \hline \multirow{3}{*}{**AUC + 95\% IC**} & \multirow{3}{*}{**Modified CNN**} & \multirow{3}{*}{**KNN**} & \multirow{3}{*}{**RF**} & \multirow{3}{*}{**GB**} & \multirow{3}{*}{**SVM**} & \multicolumn{1}{c}{
\begin{tabular}{c} **Majority** \\ **Voting * \\ \end{tabular} } \\  & & & & & & \\ \hline \multirow{3}{*}{F1-score} & ResNet50 & 0.84 [0.70, 0.95] & 0.76 [0.60, 0.90] & 0.88 [0.76, 0.98] & 0.76 [0.58, 0.91] & 0.83 [0.67, 0.96] \\  & VGG19 & 0.86 [0.74, 0.96] & 0.86 [0.73, 0.96] & 0.86 [0.74, 0.97] & 0.79 [0.63, 0.93] & 0.85 [0.70, 0.98] \\  & DenseNet121 & 0.74 [0.57, 0.89] & 0.70 [0.52, 0.85] & 0.70 [0.51, 0.85] & 0.75 [0.59, 0.88] & 0.65 [0.50, 0.80] \\  & Xception & 0.72 [0.54, 0.88] & 0.78 [0.61, 0.92] & 0.80 [0.65, 0.94] & 0.71 [0.55, 0.86] & 0.75 [0.58, 0.92] \\ \hline \multirow{3}{*}{F1-score} & ResNet50 & 0.72 & 0.69 & 0.82 & 0.80 & 0.76 \\  & VGG19 & 0.74 & 0.75 & 0.71 & 0.85 & 0.80 \\  & DenseNet121 & 0.62 & 0.67 & 0.67 & 0.82 & 0.46 \\  & Xception & 0.62 & 0.67 & 0.70 & 0.82 & 0.67 \\ \hline \multirow{3}{*}{Sensitivity} & ResNet50 & 0.75 & 0.70 & 0.85 & 0.80 & 0.80 \\  & VGG19 & 0.85 & 0.90 & 0.85 & 0.90 & 0.80 \\  & DenseNet121 & 0.95 & 0.90 & 0.75 & 0.80 & 0.30 \\  & Xception & 0.70 & 0.90 & 0.90 & 0.75 & 0.60 \\ \hline \multirow{3}{*}{Specificity} & ResNet50 & 0.70 & 0.60 & 0.80 & 0.50 & 0.85 \\  & VGG19 & 0.60 & 0.60 & 0.70 & 0.50 & 0.90 \\ \cline{1-1}  & DenseNet121 & 0.40 & 0.30 & 0.30 & 0.70 & 1.00 \\ \cline{1-1}  & Xception & 0.60 & 0.50 & 0.60 & 0.40 & 0.90 \\ \hline \hline \end{tabular}  
* Based on KNN, RF, and GB.  
\end{table} 
Table 4: Performance metrics of the 20 hybrid models/approaches predicting EAOC vs. NEOC classification. four deep learning architectures: Xception, DenseNet121, ResNet50, and VGG19.