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Computers
Volume 14
Issue 8
10.3390/computers14080333
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Comparison of Modern Convolution and Transformer Architectures: YOLO and RT-DETR in Meniscus Diagnosis

by
Aizhan Tlebaldinova
1,
Zbigniew Omiotek
2,
Markhaba Karmenova
3,*,
Saule Kumargazhanova
1,
Saule Smailova
1,
Akerke Tankibayeva
1,
Akbota Kumarkanova
1 and
Ivan Glinskiy
1
1
School of Digital Technology and Artificial Intelligence, D.Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk 070004, Kazakhstan
2
Department of Electronics and Information Technology, Lublin University of Technology, 20-618 Lublin, Poland
3
Department of Computer Modeling and Information Technologies, S.Amanzholov East Kazakhstan University, Ust-Kamenogorsk 070002, Kazakhstan
*
Author to whom correspondence should be addressed.
Computers 2025, 14(8), 333; https://doi.org/10.3390/computers14080333 (registering DOI)
Submission received: 21 July 2025 / Revised: 11 August 2025 / Accepted: 14 August 2025 / Published: 17 August 2025
Versions Notes

Abstract

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.
Keywords: magnetic resonance imaging (MRI); meniscus tear; object detection; deep learning; YOLO models; transformer-based models magnetic resonance imaging (MRI); meniscus tear; object detection; deep learning; YOLO models; transformer-based models

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MDPI and ACS Style

Tlebaldinova, A.; Omiotek, Z.; Karmenova, M.; Kumargazhanova, S.; Smailova, S.; Tankibayeva, A.; Kumarkanova, A.; Glinskiy, I. Comparison of Modern Convolution and Transformer Architectures: YOLO and RT-DETR in Meniscus Diagnosis. Computers 2025, 14, 333. https://doi.org/10.3390/computers14080333

AMA Style

Tlebaldinova A, Omiotek Z, Karmenova M, Kumargazhanova S, Smailova S, Tankibayeva A, Kumarkanova A, Glinskiy I. Comparison of Modern Convolution and Transformer Architectures: YOLO and RT-DETR in Meniscus Diagnosis. Computers. 2025; 14(8):333. https://doi.org/10.3390/computers14080333

Chicago/Turabian Style

Tlebaldinova, Aizhan, Zbigniew Omiotek, Markhaba Karmenova, Saule Kumargazhanova, Saule Smailova, Akerke Tankibayeva, Akbota Kumarkanova, and Ivan Glinskiy. 2025. "Comparison of Modern Convolution and Transformer Architectures: YOLO and RT-DETR in Meniscus Diagnosis" Computers 14, no. 8: 333. https://doi.org/10.3390/computers14080333

APA Style

Tlebaldinova, A., Omiotek, Z., Karmenova, M., Kumargazhanova, S., Smailova, S., Tankibayeva, A., Kumarkanova, A., & Glinskiy, I. (2025). Comparison of Modern Convolution and Transformer Architectures: YOLO and RT-DETR in Meniscus Diagnosis. Computers, 14(8), 333. https://doi.org/10.3390/computers14080333

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