An update on the knee osteoarthritis severity grading using wide residual learning

Abstract

BACKGROUND: Knee Osteoarthritis (KOA) is the most common type of Osteoarthritis (OA) and it is diagnosed by physicians using a standard 0 –4 Kellgren Lawrence (KL) grading system which sets the KOA on a spectrum of 5 grades; starting from normal (0) to Severe OA (4). OBJECTIVES: In this paper, we propose a transfer learning approach of a very deep wide residual learning-based network (WRN-50-2) which is fine-tuned using X-ray plain radiographs from the Osteoarthritis Initiative (OAI) dataset to learn the KL severity grading of KOA. METHODS: We propose a data augmentation approach of OAI data to avoid data imbalance and reduce overfitting by applying it only to certain KL grades depending on their number of plain radiographs. Then we conduct experiments to test the model based on an independent testing data of original plain radiographs acquired from the OAI dataset. RESULTS: Experimental results showed good generalization power in predicting the KL grade of knee X-rays with an accuracy of 72% and Precision 74%. Moreover, using Grad-Cam, we also observed that network selected some distinctive features that describe the prediction of a KL grade of a knee radiograph. CONCLUSION: This study demonstrates that our proposed new model outperforms several other related works, and it can be further improved to be used to help radiologists make more accurate and precise diagnosis of KOA in future clinical practice.