## 4 Discussion
The developed hybrid models were ranked in terms of MLcps, and a statistical test (e.g., Delong test) was used to check whether models were equivalent or not. To our knowledge, this is the first study to classify EAOC versus NEOC and the most extensive study distinguishing EAOC vs. NEOC, assessed using histological analyses, and tumoral versus non-tumoral ovaries using CE-CT images. Another significant innovation in this study is the introduction of the MLcps weighted metric as a key performance metric, which emphasizes sensitivity and specificity. This is the first time such a metric has been used to assess a transfer learning approach in ovarian cancer classification tasks, providing a more targeted way to evaluate performance. The use of this metric helps to prioritize critical factors for medical diagnoses, setting this study apart from previous research that lacked such a tailored evaluation approach. All the investigated endpoints were confirmed by the definitive pathology. Out of the developed models, the ResNet50 model coupled with Gradient Boosting resulted in the highest F1-score of 0.82 and AUC value [95% CI] of 0.88 [0.76, 0.98] in distinguishing EAOC and NEOC. VGG19 exhibited performance comparable to ResNet50, with both surpassing DenseNet121 and Xception. The error analysis carried out using confusion matrices highlights these differences in classification more prominently. ResNet50 and VGG19 coupled with GB and majority voting achieved the highest correct classifications, reinforcing their robustness. SVM exhibited stable performance across models, particularly excelling with VGG19. Conversely, KNN consistently underperformed, highlighting its limitations in this classification task. Majority voting improved overall accuracy, suggesting the advantage of ensemble strategies in mitigating individual model biases. This performance difference can likely be attributed to variations in network depth and architectural design. Specifically, VGG19, with 19 layers, relies on a simple sequential architecture of convolutional layers, while ResNet50 features 50 layers and leverages residual connections to combat vanishing gradients and improve feature learning in deeper networks. In contrast, DenseNet121 (121 layers) utilizes dense connectivity to encourage feature reuse, and Xception employs depth-wise separable convolutions for computational efficiency, which might trade off some of the features learning capacity. These design differences impact the networks' ability to learn and generalize effectively [29]. The performance of CNN coupled with SVM reached a similar one to the other ML approaches for distinguishing EAOC vs. NEOC except for Xception + SVM. The voting system based on KNN, RF, and GB achieved a similar performance. A similar behavior was observed for tumoral vs. non-tumoral classification. Out of the twenty models, the AUC ranged from 0.70 [0.56, 0.84] (ResNet50 + KNN) to 0.97 [0.92, 1.00] (VGG19 + SVM), while F1-score ranged from 0.78 (ResNet50 + KNN) to 0.88 (VGG19 + SVM), sensitivity from 0.72 (ResNet50 + KNN) to 0.92 (VGG19 + SVM), and specificity from 0.59 (DenseNet121 + RF) to 0.80 (VGG19 + SVM). In this endpoint, CNN combined with SVM performed excellently in distinguishing tumoral from non-tumoral ovaries, while the voting system (KNN, RF, GB) showed comparable performance across metrics and MLcps. GB achieved high MLcps scores, confirming its efficiency, while RF performed similarly to previous studies [30,31]. As expected, KNN showed the lowest performance [32]. GB and RF were further validated by comparison with SVM and majority voting, which outperformed individual models. SVM excelled in handling high-dimensional, imbalanced datasets [5] while majority voting improved accuracy and generalization by reducing model bias [11]. Analysis error through confusion matrices showed that ResNet50 and VGG19 coupled with GB and majority voting achieved the highest correct classifications, confirming their reliability. SVM also performed well, particularly with VGG19, while KNN showed weaker results.