Knee rheumatoid arthritis (RA) is the highly prevalent, chronic, progressive condition in the world. To diagnose this disease in the early stage in detail analysis with magnetic resonance (MR) image is possible. The imaging modality feature allows unbiased assessment of joint space narrowing (JSN),cartilage volume, and other vital features. This provides a fine-grained RA severity evaluation of the knee, contrasted to the benchmark, and generally used Kellgren Lawrence (KL) assessment. In this research, an intelligent system is developed to predict KL grade from the knee dataset. Our approach is based on hybrid deep learning of 50 layers (ResNet50) with skip connections. The proposed approach also uses Adam optimizer to provide learning linearity in the training stage. Our approach yields KL grade and JSN for femoral and tibial tissue with lateral and medial compartments. Furthermore, the approach also yields area under curve (AUC) of 0.98, accuracy 96.85%, mean absolute error (MAE) 0.015, precision 98.31%, and other commonly used parameters for the existence of radiographic RA progression which is improved than the existing state-of-the-art.

中文翻译：

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用于自动类风湿性关节炎严重程度分级的通用深度学习框架

膝关节类风湿性关节炎 (RA) 是世界上高度流行的慢性进行性疾病。通过磁共振 (MR) 图像进行详细分析，可以在早期诊断出这种疾病。成像模式功能允许对关节间隙变窄 (JSN)、软骨体积和其他重要特征进行公正评估。与基准相比，这提供了对膝关节的细粒度 RA 严重性评估，并且通常使用凯尔格伦劳伦斯 (KL) 评估。在这项研究中，开发了一个智能系统来预测膝关节数据集的 KL 等级。我们的方法基于具有跳过连接的 50 层混合深度学习 (ResNet50)。所提出的方法还使用 Adam 优化器在训练阶段提供学习线性。我们的方法为具有外侧和内侧隔室的股骨和胫骨组织产生 KL 级和 JSN。此外，该方法还产生了 0.98 的曲线下面积 (AUC)，准确度 96.85%，平均绝对误差 (MAE) 0.015，精确度 98.31%，以及比现有状态有所改善的放射影像 RA 进展存在的其他常用参数- 最先进的。