UD-TN: A comprehensive ultrasound dataset for benign and malignant thyroid nodule classification

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 containing 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 demonstrate 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 .