Yilmaz, UsameSolak, Fatma Z.2026-02-102026-02-1020261532-06261532-0634https://doi.org/10.1002/cpe.70546https://hdl.handle.net/20.500.13091/12987Knee osteoarthritis (KOA) grading from x-ray images is important for supporting effective treatment planning. Yet it remains difficult due to the disease's complex presentation. Subtle anatomical changes add to the challenge. The subjectivity of manual evaluation further complicates the process. Such challenges highlight the importance of automated, objective, and reproducible computer-aided systems capable of leveraging complementary sources of information. In line with this, a joint fusion framework was developed to integrate optimized traditional image features with deep learning representations obtained from multiple pre-trained convolutional neural network models. Traditional features, including morphological, statistical, texture-based, and other clinically relevant descriptors, provide interpretable insights into bone structure and tissue characteristics. In parallel, deep features capture intricate spatial patterns and semantic details beyond the reach of manual modeling. For improved discrimination and efficiency, analysis of variance and linear discriminant analysis were used for selecting traditional features, while principal component analysis was applied to deep features to retain 85% variance. The two feature sets were combined using a Joint Fusion Type II approach, and class imbalance was mitigated through synthetic minority oversampling. A neural network was trained to capture interdependencies between these features. Experimental results on a benchmark KOA dataset indicated that fusion with VGG16 deep features achieved 85.39% accuracy, outperforming individual feature-based approaches. The framework maintained relatively high accuracy across all five KOA grades, including borderline cases, indicating its potential for consistent and clinically relevant KOA grading.eninfo:eu-repo/semantics/closedAccessDeep LearningJoint FusionKnee Osteoarthritis GradingTraditional Image FeaturesArticle10.1002/cpe.705462-s2.0-105027513541