Deep learning in gonarthrosis classification: a comparative study of model architectures and single vs. multi-model methods
PurposeThis study aims to classify Kellgren–Lawrence (KL) osteoarthritis stages using knee anteroposterior X-ray images by comparing two deep learning (DL) methodologies: a traditional single-model approach and a proposed multi-model approach. We addressed three core research questions in this study...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1413820/full |
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| author | Sahika Betul Yayli Kutay Kılıç Salih Beyaz |
| author_facet | Sahika Betul Yayli Kutay Kılıç Salih Beyaz |
| author_sort | Sahika Betul Yayli |
| collection | DOAJ |
| description | PurposeThis study aims to classify Kellgren–Lawrence (KL) osteoarthritis stages using knee anteroposterior X-ray images by comparing two deep learning (DL) methodologies: a traditional single-model approach and a proposed multi-model approach. We addressed three core research questions in this study: (1) How effective are single-model and multi-model deep learning approaches in classifying KL stages? (2) How do seven convolutional neural network (CNN) architectures perform across four distinct deep learning tasks? (3) What is the impact of CLAHE (Contrast Limited Adaptive Histogram Equalization) on classification performance?ApproachWe created a dataset of 14,607 annotated knee AP X-rays from three hospitals. The knee joint region was isolated using a YOLOv5 object detection model. The multi-model approach utilized three DL models: one for osteophyte detection, another for joint space narrowing analysis, and a third to combine these outputs with demographic and image data for KL classification. The single-model approach directly classified KL stages as a benchmark. Seven CNN architectures (NfNet-F0/F1, EfficientNet-B0/B3, Inception-ResNet-v2, VGG16) were trained with and without CLAHE augmentation.ResultsThe single-model approach achieved an F1-score of 0.763 and accuracy of 0.767, outperforming the multi-model strategy, which scored 0.736 and 0.740. Different models performed best across tasks, underscoring the need for task-specific architecture selection. CLAHE negatively impacted most models, with only one showing a marginal improvement of 0.3%.ConclusionThe single-model approach was more effective for KL grading, surpassing metrics in existing literature. These findings emphasize the importance of task-specific architectures and preprocessing. Future studies should explore ensemble modeling, advanced augmentations, and clinical validation to enhance applicability. |
| format | Article |
| id | doaj-art-1007e528f0604af6998a3b34c8e957bc |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-1007e528f0604af6998a3b34c8e957bc2025-02-05T07:31:47ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-02-01810.3389/frai.2025.14138201413820Deep learning in gonarthrosis classification: a comparative study of model architectures and single vs. multi-model methodsSahika Betul Yayli0Kutay Kılıç1Salih Beyaz2Artificial Intelligence and Digital Analytics Solutions, Turkcell Technology, Istanbul, TürkiyeArtificial Intelligence and Digital Analytics Solutions, Turkcell Technology, Istanbul, TürkiyeOrthopedics and Traumatology Department, Adana Turgut Noyan Research and Training Centre, Baskent University, Adana, TürkiyePurposeThis study aims to classify Kellgren–Lawrence (KL) osteoarthritis stages using knee anteroposterior X-ray images by comparing two deep learning (DL) methodologies: a traditional single-model approach and a proposed multi-model approach. We addressed three core research questions in this study: (1) How effective are single-model and multi-model deep learning approaches in classifying KL stages? (2) How do seven convolutional neural network (CNN) architectures perform across four distinct deep learning tasks? (3) What is the impact of CLAHE (Contrast Limited Adaptive Histogram Equalization) on classification performance?ApproachWe created a dataset of 14,607 annotated knee AP X-rays from three hospitals. The knee joint region was isolated using a YOLOv5 object detection model. The multi-model approach utilized three DL models: one for osteophyte detection, another for joint space narrowing analysis, and a third to combine these outputs with demographic and image data for KL classification. The single-model approach directly classified KL stages as a benchmark. Seven CNN architectures (NfNet-F0/F1, EfficientNet-B0/B3, Inception-ResNet-v2, VGG16) were trained with and without CLAHE augmentation.ResultsThe single-model approach achieved an F1-score of 0.763 and accuracy of 0.767, outperforming the multi-model strategy, which scored 0.736 and 0.740. Different models performed best across tasks, underscoring the need for task-specific architecture selection. CLAHE negatively impacted most models, with only one showing a marginal improvement of 0.3%.ConclusionThe single-model approach was more effective for KL grading, surpassing metrics in existing literature. These findings emphasize the importance of task-specific architectures and preprocessing. Future studies should explore ensemble modeling, advanced augmentations, and clinical validation to enhance applicability.https://www.frontiersin.org/articles/10.3389/frai.2025.1413820/fullartificial intelligencedeep learningtransfer learningKellgren–Lawrencegonarthrosismedical imaging |
| spellingShingle | Sahika Betul Yayli Kutay Kılıç Salih Beyaz Deep learning in gonarthrosis classification: a comparative study of model architectures and single vs. multi-model methods Frontiers in Artificial Intelligence artificial intelligence deep learning transfer learning Kellgren–Lawrence gonarthrosis medical imaging |
| title | Deep learning in gonarthrosis classification: a comparative study of model architectures and single vs. multi-model methods |
| title_full | Deep learning in gonarthrosis classification: a comparative study of model architectures and single vs. multi-model methods |
| title_fullStr | Deep learning in gonarthrosis classification: a comparative study of model architectures and single vs. multi-model methods |
| title_full_unstemmed | Deep learning in gonarthrosis classification: a comparative study of model architectures and single vs. multi-model methods |
| title_short | Deep learning in gonarthrosis classification: a comparative study of model architectures and single vs. multi-model methods |
| title_sort | deep learning in gonarthrosis classification a comparative study of model architectures and single vs multi model methods |
| topic | artificial intelligence deep learning transfer learning Kellgren–Lawrence gonarthrosis medical imaging |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1413820/full |
| work_keys_str_mv | AT sahikabetulyayli deeplearningingonarthrosisclassificationacomparativestudyofmodelarchitecturesandsinglevsmultimodelmethods AT kutaykılıc deeplearningingonarthrosisclassificationacomparativestudyofmodelarchitecturesandsinglevsmultimodelmethods AT salihbeyaz deeplearningingonarthrosisclassificationacomparativestudyofmodelarchitecturesandsinglevsmultimodelmethods |