Thyroid intelligent diagnosis based on THMSNet.

BACKGROUND: Thyroid disease is a common endocrine disorder, with the differentiation between benign and malignant nodules being critical for clinical decision-making. Traditional diagnostic methods, such as ultrasound and TI-RADS classification, are limited by interobserver variability and time-consuming processes. While deep learning approaches such as CNNs and transformers have shown promise, they face challenges in multiscale feature extraction, global dependency modeling, and alignment with clinical standards.

METHODS: We proposes THMSNet, a hybrid architecture that integrates a pyramid structure for multiscale feature extraction and Mamba for global long-range dependency modeling. The serial channel-spatial attention module (SCSAM) enhances feature representation, whereas the truth-value calibration (TVC) algorithm aligns model predictions with pathological standards. The system is evaluated on a public dataset of 7,288 thyroid ultrasound images (3,282 benign, 4,006 malignant) via five metrics: accuracy, precision, recall, F1 score, and AUROC.

RESULTS: THMSNet achieves 91.15% accuracy, 93.28% recall, and 96.92% AUROC, outperforming ResNet (86.03% accuracy) and DenseNet (95.50% AUROC). Confidence intervals are calculated for key metrics, further strengthening the rigor of results. Ablation studies confirm the utility of each module, with the pyramid architecture (+7.83% accuracy), Mamba (+2.99%), SCSAM (+6.94%), and TVC (+6.94%) progressively contributing to performance improvements.

CONCLUSION: THMSNet provides a robust and clinically applicable solution for thyroid nodule diagnosis, combining advanced feature extraction, attention mechanisms, and probability calibration. Its high accuracy and interpretability make it a valuable tool for assisting radiologists in clinical practice.