Anterior cruciate ligament tear detection based on Res2Net modified by improved Lévy flight distribution
Abstract Anterior Cruciate Ligament (ACL) tears are common in sports and can provide noteworthy health issues. Therefore, accurately diagnosing of tears is important for the early and proper treatment. However, traditional diagnostic methods, such as clinical assessments and MRI, have limitations in...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-05777-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849335091988267008 |
|---|---|
| author | Peiji Yang Yanan Liu Fei Liu Mingxia Han Yadegar Abdi |
| author_facet | Peiji Yang Yanan Liu Fei Liu Mingxia Han Yadegar Abdi |
| author_sort | Peiji Yang |
| collection | DOAJ |
| description | Abstract Anterior Cruciate Ligament (ACL) tears are common in sports and can provide noteworthy health issues. Therefore, accurately diagnosing of tears is important for the early and proper treatment. However, traditional diagnostic methods, such as clinical assessments and MRI, have limitations in terms of accuracy and efficiency. This study introduces a new diagnostic approach by combining of the deep learning architecture Res2Net with an improved version of the Lévy flight distribution (ILFD) to improve the detection of ACL tears in knee MRI images. The Res2Net model is known for its ability to extract important features and classify them effectively. By optimizing the model using the ILFD algorithm, the diagnostic efficiency is greatly improved. For validation of the proposed model’s efficiency, it has been applied into two standard datasets including Stanford University Medical Center and Clinical Hospital Centre Rijeka. Comparative analysis with existing diagnostic methods, including 14 layers ResNet-14, Compact Parallel Deep Convolutional Neural Network (CPDCNN), Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), and combined CNN and Modified Golden Search Algorithm (CNN/MGSA) shows that the suggested Res2Net/ILFD model performs better in various metrics, including precision, recall, accuracy, f1-score, and specificity, and Matthews correlation coefficient. |
| format | Article |
| id | doaj-art-613d308a3e2d4cccb497eb78f9a1940e |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-613d308a3e2d4cccb497eb78f9a1940e2025-08-20T03:45:24ZengNature PortfolioScientific Reports2045-23222025-07-0115111610.1038/s41598-025-05777-5Anterior cruciate ligament tear detection based on Res2Net modified by improved Lévy flight distributionPeiji Yang0Yanan Liu1Fei Liu2Mingxia Han3Yadegar Abdi4Department of Public Physical Education, Guangxi Police CollegeShandong Transport Vocational CollegeShandong Transport Vocational CollegeShandong Transport Vocational CollegeAhar Branch, Islamic Azad UniversityAbstract Anterior Cruciate Ligament (ACL) tears are common in sports and can provide noteworthy health issues. Therefore, accurately diagnosing of tears is important for the early and proper treatment. However, traditional diagnostic methods, such as clinical assessments and MRI, have limitations in terms of accuracy and efficiency. This study introduces a new diagnostic approach by combining of the deep learning architecture Res2Net with an improved version of the Lévy flight distribution (ILFD) to improve the detection of ACL tears in knee MRI images. The Res2Net model is known for its ability to extract important features and classify them effectively. By optimizing the model using the ILFD algorithm, the diagnostic efficiency is greatly improved. For validation of the proposed model’s efficiency, it has been applied into two standard datasets including Stanford University Medical Center and Clinical Hospital Centre Rijeka. Comparative analysis with existing diagnostic methods, including 14 layers ResNet-14, Compact Parallel Deep Convolutional Neural Network (CPDCNN), Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), and combined CNN and Modified Golden Search Algorithm (CNN/MGSA) shows that the suggested Res2Net/ILFD model performs better in various metrics, including precision, recall, accuracy, f1-score, and specificity, and Matthews correlation coefficient.https://doi.org/10.1038/s41598-025-05777-5Anterior cruciate ligamentTear diagnosisRes2NetOptimizationImproved lévy flight distributionMRI images |
| spellingShingle | Peiji Yang Yanan Liu Fei Liu Mingxia Han Yadegar Abdi Anterior cruciate ligament tear detection based on Res2Net modified by improved Lévy flight distribution Scientific Reports Anterior cruciate ligament Tear diagnosis Res2Net Optimization Improved lévy flight distribution MRI images |
| title | Anterior cruciate ligament tear detection based on Res2Net modified by improved Lévy flight distribution |
| title_full | Anterior cruciate ligament tear detection based on Res2Net modified by improved Lévy flight distribution |
| title_fullStr | Anterior cruciate ligament tear detection based on Res2Net modified by improved Lévy flight distribution |
| title_full_unstemmed | Anterior cruciate ligament tear detection based on Res2Net modified by improved Lévy flight distribution |
| title_short | Anterior cruciate ligament tear detection based on Res2Net modified by improved Lévy flight distribution |
| title_sort | anterior cruciate ligament tear detection based on res2net modified by improved levy flight distribution |
| topic | Anterior cruciate ligament Tear diagnosis Res2Net Optimization Improved lévy flight distribution MRI images |
| url | https://doi.org/10.1038/s41598-025-05777-5 |
| work_keys_str_mv | AT peijiyang anteriorcruciateligamentteardetectionbasedonres2netmodifiedbyimprovedlevyflightdistribution AT yananliu anteriorcruciateligamentteardetectionbasedonres2netmodifiedbyimprovedlevyflightdistribution AT feiliu anteriorcruciateligamentteardetectionbasedonres2netmodifiedbyimprovedlevyflightdistribution AT mingxiahan anteriorcruciateligamentteardetectionbasedonres2netmodifiedbyimprovedlevyflightdistribution AT yadegarabdi anteriorcruciateligamentteardetectionbasedonres2netmodifiedbyimprovedlevyflightdistribution |