Transformer-Driven Semantic Segmentation for Thyroid Ultrasound: A SwinUNet-Based Architecture with Integrated Attention

Accurate segmentation of thyroid nodules on ultrasound images remains a challenging task in computer-aided diagnosis (CAD) mainly because of low contrast, speckle noise, and large inter-patient variability of nodule appearance. Here a new deep learning-based segmentation method has been developed on the SwinUNet architecture supported by spatial attention mechanisms to enhance feature discrimination and localization accuracy. The model takes advantage of the hierarchical feature extraction ability of the Swin Transformer to learn both global context and local fine-grained details, whereas attention modules during the decoder process selectively highlight informative areas and suppresses irrelevant background features. We checked out the system's design using the TN3K thyroid ultrasound info that's out there. It got better as it trained, peaking around the 800th run with some good numbers: a Dice Similarity Coefficient (F1 Score) of 85.51%, Precision of 87.05%, Recall of 89.13%, IoU of 78.00%, Accuracy of 97.02%, and an AUC of 99.02%. These numbers are way better than when we started (like a 15.38% jump in IoU and a 12.05% rise in F1 Score), which proves the system can learn tricky shapes and edges well. The longer it trains, the better it gets at spotting even hard-to-see thyroid lumps. This SwinUnet_withAttention thing seems to work great and could be used in clinics to help doctors figure out thyroid problems.