Optimizing Deep Learning Models for Ovarian Cancer Subtype Classification: A Systematic Evaluation of Architectures and Data Augmentation Strategies

Ovarian cancer is a leading cause of cancer-related mortality among women, and accurate classification of its subtypes is critical for effective treatment planning. This study systematically investigates the impact of different network architectures and data augmentation strategies on ovarian cancer subtype classification. We evaluate two baseline models (VGG and ViT) and propose an efficient hybrid model that integrates convolutional and self-attention mechanisms to balance local feature extraction and global context modeling. Furthermore, we conduct a comprehensive assessment of various data augmentation techniques, including geometric, color, and spatial transformations, to determine their effects on model generalization. Additionally, we compare pre-trained and non-pre-trained models to analyze the benefits of transfer learning in this domain. To enhance interpretability, we utilize Grad-CAM visualizations to examine the decision-making processes of different models. Our findings reveal that while ViT exhibits superior generalization capabilities with pre-training, VGG remains competitive even without pre-training due to its strong inductive biases. Among the tested augmentation strategies, geometric and spatial transformations significantly improve model performance, whereas color-based augmentations show limited benefits or even degrade performance. The proposed hybrid model achieves comparable classification accuracy to pre-trained baseline models while maintaining a smaller parameter scale and faster training efficiency. In conclusion, this study provides key insights into the selection of network architectures and data augmentation techniques for pathological image classification. The proposed model design framework offers an efficient and interpretable approach for ovarian cancer subtype classification, with potential applications in broader medical imaging tasks.