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 compromisi...
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| Language: | English |
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Tabriz University of Medical Sciences
2024-09-01
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| Series: | BioImpacts |
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| Online Access: | https://bi.tbzmed.ac.ir/PDF/bi-15-30419.pdf |
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| author | Yan Fang Lu Liu Qingyu Yang Shuang Hao Zhihai Luo |
| author_facet | Yan Fang Lu Liu Qingyu Yang Shuang Hao Zhihai Luo |
| author_sort | Yan Fang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ecd673efd8044b3092f5c586142ce3a1 |
| institution | DOAJ |
| issn | 2228-5652 2228-5660 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Tabriz University of Medical Sciences |
| record_format | Article |
| series | BioImpacts |
| spelling | doaj-art-ecd673efd8044b3092f5c586142ce3a12025-08-20T02:47:46ZengTabriz University of Medical SciencesBioImpacts2228-56522228-56602024-09-01151304193041910.34172/bi.30419bi-30419A new method for early diagnosis and treatment of meniscus injury of knee joint in student physical fitness tests based on deep learning methodYan Fang0Lu Liu1Qingyu Yang2Shuang Hao3Zhihai Luo4Chengdu University of Information Technology, Chengdu City 610225, ChinaChengdu University of Information Technology, Chengdu City 610225, ChinaSchool of Physical Education and Health, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, ChinaSchool of Physical Education and Health, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, ChinaChengdu Jinchen Technology Co., Ltd., Chengdu 611137, ChinaIntroduction: 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.https://bi.tbzmed.ac.ir/PDF/bi-15-30419.pdfmeniscus injuryathletes’ knee jointimproved u-netmultistage classificationensemble classification |
| spellingShingle | Yan Fang Lu Liu Qingyu Yang Shuang Hao Zhihai Luo A new method for early diagnosis and treatment of meniscus injury of knee joint in student physical fitness tests based on deep learning method BioImpacts meniscus injury athletes’ knee joint improved u-net multistage classification ensemble classification |
| title | A new method for early diagnosis and treatment of meniscus injury of knee joint in student physical fitness tests based on deep learning method |
| title_full | A new method for early diagnosis and treatment of meniscus injury of knee joint in student physical fitness tests based on deep learning method |
| title_fullStr | A new method for early diagnosis and treatment of meniscus injury of knee joint in student physical fitness tests based on deep learning method |
| title_full_unstemmed | A new method for early diagnosis and treatment of meniscus injury of knee joint in student physical fitness tests based on deep learning method |
| title_short | A new method for early diagnosis and treatment of meniscus injury of knee joint in student physical fitness tests based on deep learning method |
| title_sort | new method for early diagnosis and treatment of meniscus injury of knee joint in student physical fitness tests based on deep learning method |
| topic | meniscus injury athletes’ knee joint improved u-net multistage classification ensemble classification |
| url | https://bi.tbzmed.ac.ir/PDF/bi-15-30419.pdf |
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