Knee Osteoarthritis Severity Prediction using an Attentive Multi-Scale Deep Convolutional Neural Network

• URL: http://arxiv.org/abs/2106.14292v1
• Date: Sun, 27 Jun 2021 17:29:46 GMT
• Title: Knee Osteoarthritis Severity Prediction using an Attentive Multi-Scale Deep Convolutional Neural Network
• Authors: Rohit Kumar Jain, Prasen Kumar Sharma, Sibaji Gaj, Arijit Sur and Palash Ghosh
• Abstract summary: This paper presents a deep learning-based framework, namely OsteoHRNet, that automatically assesses the Knee Osteoarthritis severity in terms of Kellgren and Lawrence grade classification from X-rays. 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.
• Score: 8.950918531231158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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