With the widespread application of deep learning techniques in medical image processing, precise thyroid segmentation is becoming increasingly important for disease diagnosis and treatment. This study proposes a SwinTransCAD model that integrates the Swin Transformer and a multi-scale attention decoding mechanism, effectively capturing the details of the thyroid to achieve precise segmentation. The study first outlines the clinical need for thyroid disease diagnosis and the limitations of traditional segmentation methods. Then the technical features of Swin Transformer and its potential applications in medical image processing are analyzed. Finally, it provides a detailed introduction to the structure of the SwinTransCAD model and the multi-scale attention decoding mechanism. Through comparative experiments, the generalizability of the model across different datasets and its advantages in various evaluation metrics are validated. Experimental results show that the proposed method outperforms existing technologies, providing technical support for the pre-diagnosis and auxiliary treatment of thyroid diseases.