Neck lymph node detection is crucial for early cancer metastasis detection and treatment, influencing treatment success and patient survival rates. It also aids in disease staging, monitoring, and treatment selection. It requires the expertise of professional senior radiologists, as the accuracy of current automated detection methods is not sufficiently high. In this study, the neck lymph node detection network (LymoNet) based on YOLOv8 is proposed to detect and classify normal, inflammatory, and metastatic neck lymph nodes from ultrasound images. The advanced attention mechanism modules are utilized to enhance performance of the model, including the Coordinate Attention (CA) which helps the network focus on learning key features in the images, and the Multi-Head Self-Attention (MHSA) which captures global information at different scales. Meanwhile, the medical knowledge embedding which introduces prior knowledge from the medical domain is used to improve the classification performance. By integrating these elements, the YOLOv8 network can achieve better performance in neck lymph node detection tasks. Finally, LymoNet surpassed the benchmark model YOLOv8 by 6.6% in the mAP@.5, achieving the state-of-the-art (SOTA). This model provides a promising solution for automated neck lymph node detection in clinical environments. The proposed methods can also serve as a reference for applying deep learning algorithms in other fields. The source codes, trained weights, and validation data are available on GitHub.