Şahika Betül Yaylı ¹ Kutay Kılıç ¹ Salih Beyaz ^2*

• ¹ Turkcell (Turkey), Istanbul, Türkiye
• ² Başkent University, Ankara, Türkiye

Purpose: This 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. Specifically, we investigated:The effectiveness of single-model and multi-model DL approaches in KL stage classification.The performance of seven convolutional neural network (CNN) architectures across four DL tasks.The impact of CLAHE (Contrast Limited Adaptive Histogram Equalization) augmentation on classification outcomes.Approach: We 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.Results: The 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%.Conclusion: The 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.