{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T23:05:22Z","timestamp":1773183922262,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,17]],"date-time":"2025-08-17T00:00:00Z","timestamp":1755388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["AP23486396"],"award-info":[{"award-number":["AP23486396"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The aim of this study is a comparative evaluation of the effectiveness of YOLO and RT-DETR family models for the automatic recognition and localization of meniscus tears in knee joint MRI images. The experiments were conducted on a proprietary annotated dataset consisting of 2000 images from 2242 patients from various clinics. Based on key performance metrics, the most effective representatives from each family, YOLOv8-x and RT-DETR-l, were selected. Comparative analysis based on training, validation, and testing results showed that YOLOv8-x delivered more stable and accurate outcomes than RT-DETR-l. The YOLOv8-x model achieved high values across key metrics: accuracy\u20140.958, recall\u20140.961; F1-score\u20140.960; mAP@50\u20140.975; and mAP@50\u201395\u20140.616. These results demonstrate the potential of modern object detection models for clinical application, providing accurate, interpretable, and reproducible diagnosis of meniscal injuries.<\/jats:p>","DOI":"10.3390\/computers14080333","type":"journal-article","created":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T13:28:22Z","timestamp":1755523702000},"page":"333","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Comparison of Modern Convolution and Transformer Architectures: YOLO and RT-DETR in Meniscus Diagnosis"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1271-0352","authenticated-orcid":false,"given":"Aizhan","family":"Tlebaldinova","sequence":"first","affiliation":[{"name":"School of Digital Technology and Artificial Intelligence, D.Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk 070004, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6614-7799","authenticated-orcid":false,"given":"Zbigniew","family":"Omiotek","sequence":"additional","affiliation":[{"name":"Department of Electronics and Information Technology, Lublin University of Technology, 20-618 Lublin, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3028-9461","authenticated-orcid":false,"given":"Markhaba","family":"Karmenova","sequence":"additional","affiliation":[{"name":"Department of Computer Modeling and Information Technologies, S.Amanzholov East Kazakhstan University, Ust-Kamenogorsk 070002, Kazakhstan"}]},{"given":"Saule","family":"Kumargazhanova","sequence":"additional","affiliation":[{"name":"School of Digital Technology and Artificial Intelligence, D.Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk 070004, Kazakhstan"}]},{"given":"Saule","family":"Smailova","sequence":"additional","affiliation":[{"name":"School of Digital Technology and Artificial Intelligence, D.Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk 070004, Kazakhstan"}]},{"given":"Akerke","family":"Tankibayeva","sequence":"additional","affiliation":[{"name":"School of Digital Technology and Artificial Intelligence, D.Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk 070004, Kazakhstan"}]},{"given":"Akbota","family":"Kumarkanova","sequence":"additional","affiliation":[{"name":"School of Digital Technology and Artificial Intelligence, D.Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk 070004, Kazakhstan"}]},{"given":"Ivan","family":"Glinskiy","sequence":"additional","affiliation":[{"name":"School of Digital Technology and Artificial Intelligence, D.Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk 070004, Kazakhstan"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s00296-019-04489-0","article-title":"Reliability of meniscus tear description: A study using MRI from the Osteoarthritis Initiative","volume":"40","author":"Hoover","year":"2020","journal-title":"Rheumatol. Int."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1007\/s11547-022-01527-z","article-title":"MRI evaluation of meniscal anatomy: Which parameters reach the best inter-observer concordance?","volume":"127","author":"Grasso","year":"2022","journal-title":"Radiol. Med."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bien, N., Rajpurkar, P., Ball, R.L., Irvin, J., Park, A., Jones, E., Bereket, M., Patel, B.N., Yeom, K.W., and Shpanskaya, K. (2018). Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet. PLoS Med., 15.","DOI":"10.1371\/journal.pmed.1002699"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1002\/ksa.12369","article-title":"Achieving High Accuracy in Meniscus Tear Detection Using Advanced Deep Learning Models with a Relatively Small Data Set","volume":"33","author":"Vehbi","year":"2025","journal-title":"Knee Surg. Sports Traumatol. Arthrosc."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020, January 23\u201328). End-to-End Object Detection with Transformers. Proceedings of the European Conference on Computer Vision (ECCV 2020), Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., and Xu, D. (2022, January 3\u20138). UNETR: Transformers for 3D medical image segmentation. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV 2022), Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"ref_7","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., and Zhou, Y. (2021). TransUNet: Transformers make strong encoders for medical image segmentation. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2379","DOI":"10.1002\/mrm.26841","article-title":"Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging","volume":"79","author":"Liu","year":"2018","journal-title":"Magn Reson. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.diii.2019.03.002","article-title":"Automatic knee meniscus tear detection and orientation classification with Mask-RCNN","volume":"100","author":"Couteaux","year":"2019","journal-title":"Diagn. Interv. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kuczy\u0144ski, N., Bo\u015b, J., Bia\u0142osk\u00f3rska, K., Aleksandrowicz, Z., Turo\u0144, B., Zabrzy\u0144ska, M., Bonowicz, K., and Gagat, M. (2025). The Meniscus: Basic Science and Therapeutic Approaches. J. Clin. Med., 14.","DOI":"10.3390\/jcm14062020"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6590","DOI":"10.1007\/s00330-024-10706-7","article-title":"ESR Essentials: MRI of the Knee\u2014Practice Recommendations by ESSR","volume":"34","author":"Parkar","year":"2024","journal-title":"Eur. Radiol."},{"key":"ref_12","unstructured":"Smirnov, V.V., Savvova, M.V., and Smirnov, V.V. (2022). Magnetic Resonance Imaging in the Diagnosis of Joint Diseases, Artifex Publishing House. (In Russian)."},{"key":"ref_13","first-page":"102912","article-title":"Object detection from UAV thermal infrared images and videos using YOLO models","volume":"112","author":"Jiang","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","unstructured":"(2025, June 01). Ultralytics. YOLOv5. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_16","unstructured":"Hidayatullah, P., and Tubagus, R. (2025, June 01). YOLOv9 Architecture Explained | Stunning Vision AI. Available online: https:\/\/article.stunningvisionai.com\/yolov9-architecture."},{"key":"ref_17","unstructured":"Wang, Y., Li, K., Zhang, Y., Han, J., and Wang, C. (2024). YOLOv10: Real-Time End-to-End Object Detection. arXiv."},{"key":"ref_18","unstructured":"Khanam, R., and Hussain, M. (2024). YOLOv11: An Overview of the Key Architectural Enhancements. arXiv."},{"key":"ref_19","unstructured":"Tian, Y., Ye, Q., and Doermann, D. (2025). Yolov12: Attention-Centric Real-Time Object Detectors. arXiv, Available online: https:\/\/github.com\/sunsmarterjie\/yolov12."},{"key":"ref_20","unstructured":"Hidayatullah, P., Syakrani, N., Sholahuddin, M.R., Gelar, T., and Tubagus, R. (2024). YOLOv8 to YOLO11: A comprehensive architecture in-depth comparative review. arXiv."},{"key":"ref_21","unstructured":"Glenn, J. (2025, June 15). Shortcut in Backbone and Neck Issue #1200 Ultralytics\/Ultralytics. Available online: https:\/\/github.com\/ultralytics\/ultralytics\/issues\/1200#issuecomment-1454873251."},{"key":"ref_22","unstructured":"Glenn, J. (2025, June 15). Understanding SPP and SPPF Implementation Issue #8785 Ultralytics\/yolov5. Available online: https:\/\/github.com\/ultralytics\/yolov5\/issues\/8785."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hu, J., Zheng, J., Wan, W., Zhou, Y., and Huang, Z. (2025). RT-DETR-EVD: An Emergency Vehicle Detection Method Based on Improved RT-DETR. Sensors, 25.","DOI":"10.3390\/s25113327"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chen, J., Lei, B., Song, Q., Ying, H., Chen, D.Z., and Wu, J. (2020, January 13\u201319). A hierarchical graph network for 3d object detection on point clouds. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00047"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Caron, M., Touvron, H., Misra, I., J\u00e9gou, H., Mairal, J., Bojanowski, P., and Joulin, A. (2021, January 10\u201317). Emerging properties in self-supervised vision transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1186\/s44147-024-00411-z","article-title":"Performance analysis of deep learning-based object detection algorithms on COCO benchmark: A comparative study","volume":"71","author":"Tian","year":"2024","journal-title":"J. Eng. Appl. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Padilla, R., Passos, W.L., Dias, T.L.B., Netto, S.L., and da Silva, E.A.B. (2021). A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit. Electronics, 10.","DOI":"10.3390\/electronics10030279"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhao, B., Chang, L., and Liu, Z. (2025). Fast-YOLO Network Model for X-Ray Image Detection of Pneumonia. Electronics, 14.","DOI":"10.3390\/electronics14050903"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mercaldo, F., Brunese, L., Martinelli, F., Santone, A., and Cesarelli, M. (2023). Object Detection for Brain Cancer Detection and Localization. Appl. Sci., 13.","DOI":"10.3390\/app13169158"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, Q., Yan, N., Qin, Y., Zhang, X., and Li, X. (2025). BED-YOLO: An Enhanced YOLOV10N-Based Tomato Leaf Disease Detection Algorithm. Sensors, 25.","DOI":"10.3390\/s25092882"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.diii.2019.02.007","article-title":"Artificial Intelligence to Diagnose Meniscus Tears on MRI","volume":"100","author":"Roblot","year":"2019","journal-title":"Diagn. Interv. Imaging"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shin, H., Choi, G.S., Shon, O.-J., Kim, G.B., and Chang, M.C. (2022). Development of Convolutional Neural Network Model for Diagnosing Meniscus Tear Using Magnetic Resonance Image. BMC Musculoskelet. Disord., 23.","DOI":"10.1186\/s12891-022-05468-6"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.ejmp.2021.02.010","article-title":"Meniscal Lesion Detection and Characterization in Adult Knee MRI: A Deep Learning Model Approach with External Validation","volume":"83","author":"Rizk","year":"2021","journal-title":"Phys. Medica"},{"key":"ref_35","first-page":"91","article-title":"Identification and Diagnosis of Meniscus Tear by Magnetic Resonance Imaging Using a Deep Learning Model","volume":"34","author":"Li","year":"2022","journal-title":"J. Orthop. Transl."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Botnari, A., Kadar, M., and Patrascu, J.M. (2024). A Comprehensive Evaluation of Deep Learning Models on Knee MRIs for the Diagnosis and Classification of Meniscal Tears: A Systematic Review and Meta-Analysis. Diagnostics, 14.","DOI":"10.3390\/diagnostics14111090"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, L.-H., Zhou, Y.-Z., Liu, L., Cao, W., and Ma, J.-H. (2025). Research on object detection and recognition in remote sensing images based on YOLOv11. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-96314-x"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/8\/333\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:29:25Z","timestamp":1760034565000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/8\/333"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,17]]},"references-count":37,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["computers14080333"],"URL":"https:\/\/doi.org\/10.3390\/computers14080333","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,17]]}}}