Thyroid intelligent diagnosis based on THMSNet

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.

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.

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.

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.