RSA-UNet: A ResNet- and Attention-Enhanced Network for Accurate Thyroid Ultrasound Image Segmentation

Thyroid nodules are a common endocrine disorder, and accurate imaging diagnosis plays a crucial role in differentiating benign from malignant lesions. However, conventional image analysis often depends on manual annotations and physicians’ subjective experience, leading to inconsistency and variability in diagnostic outcomes. To address these challenges, this study proposes residual and spatial attention U-net (RSA-UNet), an automated segmentation model for thyroid nodule ultrasound images. The proposed architecture builds upon the U-Net framework. It integrates three key components: a ResNet50 encoder to enhance feature extraction through residual learning, a spatial channel block attention module (SCBAM) to strengthen the network’s focus on salient features, and an atrous spatial pyramid pooling (ASPP) module to capture multi-scale global contextual information. Experimental results demonstrate that RSA-UNet achieves superior segmentation performance, with an accuracy of 96.45%, a mean intersection over union (mIoU) of 85.55%, and a Dice similarity coefficient (DSC) of 91.87%, outperforming various U-Net variants. These results highlight the model’s effectiveness and its potential for clinical application in the automated assessment of thyroid nodules.