摘要The automatic classification of thyroid nodules in ultrasound images is a critical research focus in medical imaging.However,publicly available thyroid ultrasound datasets remain scarce.In this study,we developed the Ultrasound Dataset for Thyroid Nodules(UD-TN),a comprehensive dataset con-taining 10,495 labeled images classified as benign or malignant based on pathology-confirmed results.To establish a benchmark,we proposed the Thyroid Ultrasound Image Neural Network(ThyUNet),a deep learning model designed for accurate nodule classification.By incorporating high-resolution feature enhancement,instance normalization,and dilated convolutions into residual blocks,ThyUNet excels in extracting fine-grained features,particularly for small nodules.Experimental results demon-strate that ThyUNet achieves state-of-the-art performance,with an accuracy of 89.7％,a sensitivity of 0.879,and a specificity of 0.910 on the testing set.These results surpass those of other advanced architectures,highlighting the model's effectiveness.UD-TN and ThyUNet contribute significantly to advancing intelligent medical diagnostics.Dataset details and access instructions are available at https://github.com/18811755633/Sample-of-UD-TN.