BACKGROUND: Accurate segmentation of thyroid nodules in ultrasound imaging remains a significant challenge in medical diagnostics, primarily due to edge blurring and substantial variability in nodule size. These challenges directly affect the precision of thyroid disorder diagnoses, which are crucial for metabolic and hormonal regulation. METHODS: This study proposes a novel segmentation approach utilizing a Swin U-Net architecture enhanced with a self-attention mechanism. The model integrates residual and multiscale convolutional structures in the encoder path, with long skip connections feeding into an attention module to improve edge preservation and feature extraction. The decoder path employs these refined features to achieve precise segmentation. Comparative evaluations were conducted against traditional models, including U-Net and DeepLabv3+. RESULTS: The Swin U-Net model demonstrated superior performance, achieving an average Dice Similarity Coefficient (DSC) of 0.78, surpassing baseline models such as U-Net and DeepLabv3+. The incorporation of residual and multiscale convolutional structures, along with the use of long skip connections, effectively addressed issues of edge blurring and nodule size variability. These advancements resulted in significant improvements in segmentation accuracy, highlighting the model's potential for addressing the inherent challenges of thyroid ultrasound imaging. CONCLUSION: The enhanced Swin U-Net architecture exhibits notable improvements in the robustness and accuracy of thyroid nodule segmentation, offering considerable potential for clinical applications in thyroid disorder diagnosis. While the study acknowledges dataset size limitations, the findings demonstrate the effectiveness of the proposed approach. This method represents a significant step toward more reliable and precise diagnostics in thyroid disease management, with potential implications for enhanced patient outcomes in clinical practice.