Deep learning classification of ovarian tumor stage by an ensemble of neural networks

The research focuses on a deep learning ensemble algorithms approach for classifying ovarian tumor stages, using medical images and specific annotations from the TCGA-OV dataset. Ovarian cancer, a particularly dangerous form of gynecological malignancy, is often diagnosed in advanced stages. After implementing transfer techniques on 5 individual neural networks, including ResNet-50, DenseNet-12, EfficientNet-B3, Vision Transformer (ViT), and MobileNetV2, the best 3 were chosen to create an ensemble model. The ensemble model (Weighted Voting Ensemble) demonstrated an enhanced 97.2% accuracy and an AUC-ROC of 0.985 compared to the best individual neural network performances obtained for Vision Transformer (ViT) -accuracy of 95.4% and an AUC-ROC of 0.971. This approach diminishes classification errors and enhances generalization. The research emphasizes the importance of dataset quality, interpretability, and clinical relevance, proposing future efforts in improving datasets and implementing clinical AI in real-time. The results indicate that AI can greatly enhance the staging of ovarian tumors, lessening diagnostic bias and tailoring personalized treatments.