## 1 Introduction
For non-linearly separable data, SVM employs kernel functions to map inputs into a higher-dimensional space, enabling separation [5]. In particular, the SVM algorithm is the most used one reported in the literature [5; 6; 7; 8], in combination with deep learning approaches, due to its complexity (which allows one to obtain more precise results and makes it suitable to be used on small datasets); on the other hand, it is time-consuming since it requests a lot of computational resources [9]. CNNs are deep networks designed for spatially structured data such as images [10]. They consist of convolutional layers that apply filters to extract hierarchical features, followed by pooling layers to reduce dimensionality and enhance translational invariance. The final layers are typically fully connected for classification [10]. Different CNN architectures are employed for bi-dimensional image classification (i.e., tumoral vs. non-tumoral tissue). Transfer learning is widely used in medical imaging to address the challenges posed by small datasets. The most employed CNN architectures are ResNet and VGG-based models [9], which leverage residual blocks to enhance training stability and improve parameter updates in early layers. The hybrid approach combines CNN with an ML model and extracts features from clinical images by removing the CNN's final layers and using the extracted features to train the ML model, bypassing the CNN's classification stage. A further strategy to improve model performance relies on ensemble strategies, such as the majority voting approach [11]. This approach enhances robustness while reducing overfitting, leveraging the diversity among different underlying input models [12]. In this way, generalization is improved by mitigating individual model biases and limitations [13]. Both CNN and ML models rely on meticulous data processing, balancing, and validation strategies. Data are typically split into training, validation, and test sets, often following a 70:20:10 [14] ratio, ensuring comprehensive model development, hyperparameter tuning, and unbiased evaluation. Addressing data imbalance is critical, as skewed class distributions can degrade predictive accuracy. Solutions such as oversampling, undersampling, or synthetic data generation (e.g., SMOTE) help restore class balance [15]. In CNNs, data augmentation--applying geometric transformations, random cropping, or noise injection--enhances data diversity and mitigates overfitting. Cross-validation [16], e.g., 5-fold, remains a robust standard for performance estimation, optimizing generalizability. These strategies collectively underpin the reliability of ML and CNN models, which are essential for clinical decision-making applications. To date, no methods have been proposed to distinguish between women with EAOC, NEOC, or OC and benign tissues using pre-operative contrast-enhanced CT (CE-CT) images [17], which are widely used in clinical practice. Currently, a definitive diagnosis requires post-surgical evaluation by a pathologist, limiting personalized surgical approaches due to the absence of pre-surgical biomarkers for the early detection of EAOC. This study addressed this gap by developing several hybrid AI approaches to differentiate EAOC from NEOC or tumoral vs. non-tumoral lesions using pre-operative CE-CT images. A key novelty lies in the implementation of advanced data processing and balancing strategies, along with a robust model comparison framework leveraging multiple metrics to enhance reliability and predictive performance. Furthermore, the co-creation approach, involving collaboration between clinicians and AI developers, may enhance the model's usability in clinical settings.