Akademik Veri Yönetim Sistemi
PEERJ COMPUTER SCIENCE, sa.11, ss.1-31, 2025 (SCI-Expanded, Scopus)
Accurate and early diagnosis of knee osteoarthritis (KOA) is critical for effective treatment and patient management. In this study, I propose an enhanced deep learning-based diagnostic framework centered on our custom-designed DenseNet201+ architecture, specifically optimized for automated Kellgren–Lawrence (KL) grading from radiographic images. DenseNet201+ introduces architectural innovations including spatial attention mechanisms and multi-scale pooling strategies, enabling comprehensive extraction of both global anatomical patterns and localized joint features. To further enhance diagnostic performance, the methodology implements a deep feature engineering pipeline that transforms the extracted 512- dimensional feature vectors into enriched representations through higher-order statistical analysis, entropy computation, and activation modeling. These engineered features are then classified using multiple classical machine learning algorithms. Among them, a support vector machine (SVM) with a radial basis function (RBF) kernel achieved the best performance. Evaluated on a dataset of 13,254 knee X-rays with balanced class distributions across KL grades, the proposed method attained an accuracy of 94.68%, an area under the curve (AUC) of 99.40%, and a perfect AUC of 100% for severe KOA cases (Grade 4). The proposed framework demonstrates robust generalization with a cross-validation stability of 98.71% ± 0.32, and excellent inter-rater agreement (Cohen’s kappa = 0.933, intraclass correlation coefficient (ICC) = 0.951). Explainability was addressed via Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations, highlighting diagnostically relevant regions. In summary, DenseNet201+ combined with deep feature engineering and classical classification establishes a state-of-the-art, interpretable, and computationally efficient solution for automated KOA grading. Its performance exceeds that of existing models and aligns with clinical requirements, offering a scalable tool for real-time deployment in radiology workflows and remote healthcare systems.