A new method for early diagnosis and treatment of meniscus injury of knee joint in student physical fitness tests based on deep learning method.

Introduction: Meniscus injuries in athletes' knee joints not only hinder performance but also pose substantial challenges in timely diagnosis and effective treatment. Delayed or inaccurate diagnosis often leads to prolonged recovery periods, exacerbating athletes' discomfort and compromising their ability to return to peak performance levels. Therefore, the accurate and timely diagnosis of meniscus injuries is crucial for athletes to receive appropriate treatment promptly and resume their training regimen effectively. Methods: This paper presents a multi-step approach for diagnosing meniscus injuries through segmentation of images into lesions regions, followed by a combined classification method. The present study employs a method whereby image noise is first reduced, followed by the implementation of an enhanced iteration of the U-Net algorithm to perform image segmentation and identify regions of interest for potential injury detection. Results: In the context of diagnosing injury images, the extraction of features was accomplished through the utilization of the contour line method. Furthermore, the identification of injury types was facilitated through the application of the ensemble method, employing the principles of basic category-based voting. The method under consideration has been subjected to evaluation using a well-recognized dataset comprising MRI images knee joint injuries. Conclusion: The findings reveal that the efficacy of the proposed approach exhibits a significant enhancement in contrast to the newly developed techniques.