Title

Deep ensemble network for quantification and severity assessment of knee osteoarthritis

Abstract

The assessment of knee joint gap and severity of Osteoarthritis (OA) is subjective and often inaccurate. The main source of error is due to the judgement of human expert from low resolution images (i.e., X-ray images). To address the problem, we developed an ensemble of Deep Learning (DL) model to objectively score the severity of OA only from the radiometric images. The proposed method consists of two main modules. First, we developed a scale invariant and aspect ratio preserving automatic localization and characterization of the kneecap area. Second, we developed multiple instances of 'hyper parameter optimized' DL models and fused them using ensemble classification to score the severity of OA. In this implementation, we used three convolutional neural networks to improve the bias-variance trade-off, and boost accuracy and generalization. We tested our modeling framework using a collection of 4,796 X-ray images from Osteoarthritis Initiative (OAI). Our results show a higher performance (~ 2-8%) when compared to the state-of-the-art methods. Finally, this machine learning-based methodology provides a pipeline in decision support system for assessing and quantifying the OA severity.

Publication Title

Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

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