Disentanglement of Latent Factors of Radiographic Knee Images for Knee Osteoarthritis Severity Classification
Keywords:Knee Osteoarthritis, Classification, Autoencoder, Convolutional Neural Network, X-ray image
Knee osteoarthritis is the most common form of the musculoskeletal disorder that usually happens to the elderly. The diagnosis and treatment for knee osteoarthritis are causing a huge economic burden to society. Traditionally, the diagnosis for knee osteoarthritis was done by analyzing MRI (Magnetic Resonance Imaging). However, this method has problems in the way that it is costly and has limited access since they are only available in specialized medical institutions. There have been many researches that attempt to use x-ray images that are safe, cost-efficient, and commonly available. Their method often fails and shows poor results due to a lack of training dataset. In this paper, I proposed a novel autoencoder-based knee osteoarthritis classification system. Unlike the previous deep learning-based research, the proposed method disentangles the osteoarthritis-related latent factors from knee x-ray images. These latent factors are then trained to predict the severity of osteoarthritis. The proposed method achieves accuracy of 78.9% on the publicly available dataset. Our method produces accurate results without having a large dataset while successfully avoiding the curse of dimensionality. Throughout the comprehensive experiment, I have shown that the proposed method outperforms the existing state-of-the-art methods by a great accuracy margin.
References or Bibliography
Losina, Elena, et al. "Lifetime medical costs of knee osteoarthritis management in the United States: impact of extending indications for total knee arthroplasty." Arthritis care & research 67.2 (2015): 203-215.
Izenman, Alan Julian. "Introduction to manifold learning." Wiley Interdisciplinary Reviews: Computational Statistics 4.5 (2012): 439-446.
Huo, Xiaoming, Xuelei Sherry Ni, and Andrew K. Smith. "A survey of manifold-based learning methods." Recent advances in data mining of enterprise data (2007): 691-745.
Pless, Robert, and Richard Souvenir. "A survey of manifold learning for images." IPSJ Transactions on Computer Vision and Applications 1 (2009): 83-94.
Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.
He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
Lu, Dengsheng, and Qihao Weng. "A survey of image classification methods and techniques for improving classification performance." International journal of Remote sensing 28.5 (2007): 823-870.
Nath, Siddhartha Sankar, et al. "A survey of image classification methods and techniques." 2014 International conference on control, instrumentation, communication and computational technologies (ICCICCT). IEEE, 2014.
Chen, Pingjun (2018), “Knee Osteoarthritis Severity Grading Dataset”, Mendeley Data, V1
Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems 25 (2012).
Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
How to Cite
Copyright (c) 2022 Seung-woo Ko
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright holder(s) granted JSR a perpetual, non-exclusive license to distriute & display this article.