@article{4389,
  author = {Lixin Wei},
  title = {Site Analysis of Knee Joint Injuries in Games Based on Deep Learning and Fusion Networks},
  journal = {Journal of Networking Technology},
  year = {2025},
  volume = {16},
  number = {1},
  doi = {https://doi.org/10.6025/jnt/2025/16/1/25-31},
  url = {https://www.dline.info/jnt/fulltext/v16n1/jntv16n1_4.pdf},
  abstract = {By applying deep learning, we can better understand knee joint injuries that may occur in ball sports. This
method can help us prevent these injuries more effectively and provide better protection for our athletes. This
study developed a deep learning technique based on multiple modalities to identify common, major, and
meniscus tear injuries, achieving rapid and accurate diagnosis. Additionally, it effectively categorizes the
affected organs into different lesions based on their shape, size, and function. Experimental results demonstrated
that the model accurately reflects the changes in the ROC curve, with an AUC change rate of 97.89% in normal
anterior cruciate ligament tear or meniscus tear situations, significantly outperforming other methods and
providing high accuracy.},
}