The Kellgren–Lawrence (KL) grading system is a scoring system for classifying the severity of knee osteoarthritis using X-ray images, and it is the standard X-ray-based grading system for diagnosing knee osteoarthritis. However, KL grading depends on the clinician’s subjective assessment. Moreover, the accuracy varies significantly depending on the clinician’s experience and can be particularly low. Therefore, in this study, we developed an ensemble network that can predict a consistent and accurate KL grade for knee osteoarthritis severity using a deep learning approach. We trained individual models on knee X-ray datasets using the most suitable image size for each model in an ensemble network rather than using datasets with a single image size. We then built the ensemble network using these models to overcome the instability of single models and further improve accuracy. We conducted various experiments using a dataset of 8260 images from the Osteoarthritis Initiative open dataset. The proposed ensemble network exhibited the best performance, achieving an accuracy of 76.93% and an F1-score of 0.7665. The Grad-CAM visualization technique was used to further evaluate the focus of the model. The results demonstrated that the proposed ensemble network outperforms existing techniques that have performed well in KL grade classification. Moreover, the proposed model focuses on the joint space around the knee to extract the imaging features required for KL grade classification, revealing its high potential for diagnosing knee osteoarthritis.

Kellgren-Lawrence (KL) 分级系统是一种利用 X 射线图像对膝骨关节炎严重程度进行分类的评分系统，也是诊断膝骨关节炎的标准基于 X 射线的分级系统。然而，KL 分级取决于临床医生的主观评估。此外，准确度根据临床医生的经验而有很大差异，并且可能特别低。因此，在这项研究中，我们开发了一个集成网络，可以使用深度学习方法预测膝骨关节炎严重程度的一致且准确的 KL 等级。我们使用集成网络中每个模型最合适的图像尺寸，在膝盖 X 射线数据集上训练各个模型，而不是使用具有单一图像尺寸的数据集。然后，我们使用这些模型构建了集成网络，以克服单个模型的不稳定性并进一步提高准确性。我们使用来自 Osteoarthritis Initiative 开放数据集的 8260 张图像的数据集进行了各种实验。所提出的集成网络表现出最好的性能，达到 76.93% 的准确率和 0.7665 的 F1 分数。 Grad-CAM可视化技术用于进一步评估模型的焦点。结果表明，所提出的集成网络优于在 KL 等级分类中表现良好的现有技术。此外，所提出的模型侧重于膝关节周围的关节空间，以提取 KL 分级分类所需的成像特征，揭示了其诊断膝骨关节炎的巨大潜力。