OvCaNet: An uncertainty-calibrated deep learning framework for benign–malignant ovarian cancer classification from ultrasound imaging

Background Accurate differentiation between benign and malignant ovarian cancer on ultrasound remains challenging because of overlapping sonographic characteristics, severe class imbalance, and the tendency of conventional deep learning models to produce overconfident predictions. Reliable uncertainty estimation is therefore essential for developing clinically trustworthy computer-aided diagnostic systems. Methods We developed OvCaNet, an uncertainty-calibrated deep learning framework for benign–malignant ovarian tumor classification from two-dimensional ultrasound images. OvCaNet combines an ImageNet-pretrained EfficientNet-B0 backbone with a Convolutional Block Attention Module, a taxonomy-guided auxiliary head for eight-class tumor subtype learning, a Dirichlet evidential classification head, and split conformal prediction. The framework was evaluated on the public MMOTU OTU_2D dataset containing 1,469 images from 294 patients. Patient-level stratified partitioning and five-fold cross-validation were used to minimize information leakage. Performance was compared with ResNet-18, ResNet-50, DenseNet-121, MobileNetV2, and EfficientNet-B0 using discrimination, calibration, uncertainty, efficiency, and explainability measures. Results OvCaNet achieved an accuracy of 97.86%, AUC-ROC of 0.989, AUC-PR of 0.935, sensitivity of 0.947, specificity of 0.981, and F1-score of 0.939. Compared with EfficientNet-B0, the strongest baseline, OvCaNet improved AUC-PR from 0.792 to 0.935 and sensitivity from 0.804 to 0.947. It also achieved the lowest Expected Calibration Error (0.013), Brier score (0.018), and negative log-likelihood (0.071). Five-fold cross-validation produced stable performance, while ablation experiments demonstrated complementary contributions from attention, auxiliary supervision, and evidential learning. Conformal prediction achieved empirical coverage close to the predefined targets, and Grad-CAM-based analysis provided qualitative evidence of clinically relevant feature localization. Conclusion OvCaNet provides accurate, computationally efficient, and uncertainty-aware ovarian tumor classification from two-dimensional ultrasound images. The results indicate that integrating subtype-guided representation learning, evidential uncertainty estimation, and conformal prediction can improve the reliability of AI-assisted ultrasound diagnosis. Validation on larger independent and multi-center cohorts remains necessary before clinical deployment.