Knee osteoarthritis (OA) is the most common joint disorder and a leading cause of disability. Diagnosing OA severity typically requires expert assessment of X-ray images and is commonly based on the Kellgren-Lawrence grading system, a time-intensive process. This study aimed to develop an automated deep learning model to classify knee OA severity, reducing the need for expert evaluation. First, we evaluated ten state-of-the-art deep learning models, achieving a top accuracy of 0.69 with individual models. To address class imbalance, we employed weighted sampling, improving accuracy to 0.70. We further applied Smooth-GradCAM++ to visualize decision-influencing regions, enhancing the explainability of the best-performing model. Finally, we developed ensemble models using majority voting and a shallow neural network. Our ensemble model, KneeXNet, achieved the highest accuracy of 0.72, demonstrating its potential as an automated tool for knee OA assessment.

翻译：膝关节骨关节炎（OA）是最常见的关节疾病，也是导致残疾的主要原因。诊断OA严重程度通常需要专家对X光图像进行评估，并普遍基于Kellgren-Lawrence分级系统，这是一个耗时且密集的过程。本研究旨在开发一种自动化的深度学习模型来对膝关节OA严重程度进行分类，以减少对专家评估的依赖。首先，我们评估了十种先进的深度学习模型，单个模型最高准确率达到0.69。针对类别不平衡问题，我们采用加权采样方法，将准确率提升至0.70。进一步，我们应用Smooth-GradCAM++来可视化影响决策的区域，增强了最佳性能模型的可解释性。最后，我们利用多数投票法和浅层神经网络构建了集成模型。我们的集成模型KneeXNet取得了最高的准确率0.72，展示了其作为膝关节OA自动评估工具的潜力。