Knee Osteoarthritis (OA) is a destructive joint disease identified by joint stiffness, pain, and functional disability concerning millions of lives across the globe. It is generally assessed by evaluating physical symptoms, medical history, and other joint screening tests like radiographs, Magnetic Resonance Imaging (MRI), and Computed Tomography (CT) scans. Unfortunately, the conventional methods are very subjective, which forms a barrier in detecting the disease progression at an early stage. This paper presents a deep learning-based framework, namely OsteoHRNet, that automatically assesses the Knee OA severity in terms of Kellgren and Lawrence (KL) grade classification from X-rays. As a primary novelty, the proposed approach is built upon one of the most recent deep models, called the High-Resolution Network (HRNet), to capture the multi-scale features of knee X-rays. In addition, we have also incorporated an attention mechanism to filter out the counterproductive features and boost the performance further. Our proposed model has achieved the best multiclass accuracy of 71.74% and MAE of 0.311 on the baseline cohort of the OAI dataset, which is a remarkable gain over the existing best-published works. We have also employed the Gradient-based Class Activation Maps (Grad-CAMs) visualization to justify the proposed network learning.
翻译:不幸的是,传统方法非常主观,在早期就构成检测疾病发展的障碍。本文展示了一个深层次的学习基础框架,即OsteoHRNet,它从X光中自动评估Kene OA严重性,从Kellgren和Lawrence(KL)的级别分类角度评估Kene OA严重性。作为主要的新颖做法,拟议方法建立在最新的深度模型之一之上,称为高分辨率网络(HRNet),以捕捉膝部X光的多尺度特征。此外,我们还采用了关注机制,以过滤反效果特征,进一步提升性能。我们提议的模型已经实现了71.74%和0.311的Kne OA(KL)级分类的最佳多级精确度。我们采用的最新深度模型,即高分辨率网络(HRNet),以捕捉到高分辨率X光线的多级特征特征。我们提议的模型已经实现了以Kellgren和Lawrence (KL) 和Lawrence(KL) 等级分类为基准的0.311的高级级标准。我们采用的最新图像数据库(SLA-GA-GLA-S-S-S-S-S-S-S-S-S-S-S-S-S-S-SD-SD-SD-Sq-S-S-S-S-SD-S-S-S-S-S-S-S-S-S-G-S-S-S-S-S-S-S-S-S-SL-S-S-S-S-S-G-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-G-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-