  This paper proposes a new approach to automatically quantify the severity of
knee osteoarthritis (OA) from radiographs using deep convolutional neural
networks (CNN). Clinically, knee OA severity is assessed using Kellgren \&
Lawrence (KL) grades, a five point scale. Previous work on automatically
predicting KL grades from radiograph images were based on training shallow
classifiers using a variety of hand engineered features. We demonstrate that
classification accuracy can be significantly improved using deep convolutional
neural network models pre-trained on ImageNet and fine-tuned on knee OA images.
Furthermore, we argue that it is more appropriate to assess the accuracy of
automatic knee OA severity predictions using a continuous distance-based
evaluation metric like mean squared error than it is to use classification
accuracy. This leads to the formulation of the prediction of KL grades as a
regression problem and further improves accuracy. Results on a dataset of X-ray
images and KL grades from the Osteoarthritis Initiative (OAI) show a sizable
improvement over the current state-of-the-art.
