<p>Ultrasound is widely used to monitor and diagnose thyroid nodules, but accurately segmenting these nodules in ultrasound images remains a challenge due to the presence of noise and artifacts, which often blur nodule boundaries. While several deep learning algorithms have been developed for this task, their performance is frequently suboptimal. In this study, we introduce the use of EfficientNet-B7 as the backbone for the DeepLabV3+ architecture in thyroid nodule segmentation, marking its first application in this area. We evaluated the proposed method using a dataset from the First Affiliated Hospital of Zhengzhou University, along with two public datasets. The results demonstrate high performance, with a pixel accuracy (PA) of 97.67%, a Dice similarity coefficient of 0.8839, and an Intersection over Union (IoU) of 79.69%. These outcomes outperform most traditional segmentation networks.</p>