Comparison of Modern Convolution and Transformer Architectures: YOLO and RT-DETR in Meniscus Diagnosis
Tlebaldinova A. Omiotek Z. Karmenova M. Kumargazhanova S. Smailova S. Tankibayeva A. Kumarkanova A. Glinskiy I.
August 2025Multidisciplinary Digital Publishing Institute (MDPI)
Computers
2025#14Issue 8
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—0.958, recall—0.961; F1-score—0.960; mAP@50—0.975; and mAP@50–95—0.616. These results demonstrate the potential of modern object detection models for clinical application, providing accurate, interpretable, and reproducible diagnosis of meniscal injuries.
deep learning , magnetic resonance imaging (MRI) , meniscus tear , object detection , transformer-based models , YOLO models
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School of Digital Technology and Artificial Intelligence, D.Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk, 070004, Kazakhstan
Department of Electronics and Information Technology, Lublin University of Technology, Lublin, 20-618, Poland
Department of Computer Modeling and Information Technologies, S.Amanzholov East Kazakhstan University, Ust-Kamenogorsk, 070002, Kazakhstan
School of Digital Technology and Artificial Intelligence
Department of Electronics and Information Technology
Department of Computer Modeling and Information Technologies
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