A Dynamic Balanced Training Regime for Mitigating Class Imbalance in Thyroid Nodule Classification

Handling class imbalance remains a major challenge in developing intelligent diagnostic systems for ultrasound-based thyroid nodule classification. Synthetic sample generation often distorts data distributions and weakens texture fidelity, a critical concern in ultrasound, where fine anatomical details carry diagnostic value. Loss reweighting may destabilize optimization under severe imbalance, while recent approaches favor architectural complexity over training-level refinement, limiting clinical deployment. These issues hinder accurate minority-class detection in screening, where precise classification is essential to avoid missed diagnoses or unnecessary interventions. In this paper, a lightweight adaptive framework extending the Dynamic Balanced Training Regime (DBTR) is proposed for class-imbalanced thyroid ultrasound classification. First, balanced subsets are constructed directly from original ultrasound images, preserving diagnostic texture without synthetic augmentation. Then, a recall-guided learning-rate adjustment is introduced to modulate the classifier head with a fixed backbone, strengthening minority-class sensitivity under speckle noise. Experiments were conducted on the imbalanced DDTI benchmark using 10-fold cross-validation and further tested on two recent independent external cohorts, TN5000 and ThyUS2Path, acquired under different clinical settings. Internal evaluation demonstrates that the proposed framework achieves the highest AUC (0.714) among the evaluated CNN architectures, while maintaining high sensitivity (0.984) and improving specificity from 0.233 to 0.250 over the Xception baseline, reaching balanced accuracy (0.617). Under external evaluation, a 5.5% relative increase in specificity was obtained on ThyUS2Path despite domain shift, while sensitivity remained preserved across both cohorts. Computational benchmarking confirmed efficient deployment, supporting the integration of intelligent diagnostic support systems into point-of-care ultrasound screening workflows.