Toward automatic quantification of knee osteoarthritis severity using improved Faster R-CNN.

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Abstract

Knee osteoarthritis (OA) is a common disease that impairs knee function and causes pain. Radiologists usually review knee X-ray images and grade the severity of the impairments according to the Kellgren-Lawrence grading scheme. However, this approach becomes inefficient in hospitals with high throughput as it is time-consuming, tedious and also subjective. This paper introduces a model for automatic diagnosis of knee OA based on an end-to-end deep learning method.
In order to process the input images with location and classification simultaneously, we use Faster R-CNN as baseline, which consists of region proposal network (RPN) and Fast R-CNN. The RPN is trained to generate region proposals, which contain knee joint and then be used by Fast R-CNN for classification. Due to the localized classification via CNNs, the useless information in X-ray images can be filtered and we can extract clinically relevant features. For the further improvement in the model’s performance, we use a novel loss function whose weighting scheme allows us to address the class imbalance. Besides, larger anchors are used to overcome the problem that anchors don’t match the object when increasing the input size of X-ray images.
The performance of the proposed model is thoroughly assessed using various measures. The results show that our adjusted model outperforms the Faster R-CNN, achieving a mean average precision nearly 0.82 with a sensitivity above 78% and a specificity above 94%. It takes 0.33 s to test each image, which achieves a better trade-off between accuracy and speed.
The proposed end-to-end fully automatic model which is computationally efficient has the potential to achieve the real automatic diagnosis of knee OA and be used as computer-aided diagnosis tools in clinical applications.