Comparison of Modern Convolution and Transformer Architectures: YOLO and RT-DETR in Meniscus Diagnosis
Artykuł w czasopiśmie
MNiSW
20
Lista 2024
| Status: | |
| Autorzy: | Tlebaldinova Aizhan, Omiotek Zbigniew, Karmenova Markhaba, Kumargazhanova Saule, Smailova Saule, Tankibayeva Akerke , Kumarkanova Akbota, Glinskiy Ivan |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2025 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 8 |
| Wolumen/Tom: | 14 |
| Numer artykułu: | 333 |
| Strony: | 1 - 28 |
| Impact Factor: | 4,2 |
| Web of Science® Times Cited: | 1 |
| Scopus® Cytowania: | 1 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Finansowanie: | This research was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan, grant number AP23486396. |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 17 sierpnia 2025 |
| Abstrakty: | angielski |
| 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. |
