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Thyroid nodule malignancy assessment is a high-stakes predictive task in clinical radiology, where diagnostic uncertainty directly affects biopsy referral, surgical planning, and patient outcomes. We present SSMURNet, a deep learning framework for confidence-calibrated thyroid nodule classification from ultrasound images. The architecture integrates a dual-branch spectral–spatial encoder combining FFT frequency analysis with Mamba state space modelling, an Uncertainty-Guided Attention (UGA) mechanism, and a hierarchical evidential learning objective. Evaluated on 7,288 pathologically confirmed images, SSMURNet achieved 92.50% accuracy and 0.9700 AUROC. External validation on an independent dataset achieved 85.42% accuracy and 0.9185 AUROC without retraining, demonstrating robust cross-domain generalisation.

Breast cancer early detection heavily relies on ultrasound imaging, but accurate diagnosis is often hindered by speckle noise, operator dependency, and indistinct lesion boundaries. Existing deep learning methods lack hierarchical multi-scale integration, quantitative interpretability validation, and cross-dataset generalization analysis, hindering clinical adoption. In this work, we propose HyFormer-Net, a hybrid CNN-Transformer framework integrating EfficientNet-B3 and Swin Transformer via multi-scale hierarchical fusion blocks at four encoder stages. The attention-gated decoder enables dual-pipeline interpretability: intrinsic attention validation (quantitative IoU verification) and Grad-CAM classification reasoning. HyFormer-Net achieved 76.1% Dice score and 93.2% classification accuracy on BUSI, with clinically critical 92.1% Malignant Recall.