Abstract
Knee osteoarthritis severity grading from plain radiographs and magnetic resonance (MR) images is of great significance in the diagnosis of osteoarthritis (OA). Recently, deep learning had a great impact on improving the Kellgren and Lawrence (KL) grading scheme of Knee osteoarthritis KOA using models that acquire the contextual features spontaneously without the need for any conventional high computational spatial configuration modeling. In this study, we review the state-of-the-art deep learning methods that enhanced the knee osteoarthritis severity KL grading. Pre-trained models such as Resnet18, VGG, DenseNet, Convolutional Siamese neural network, ResNet34, Squeeze-and-excitation ResNet (SE-ResNet) were found to be employed to extract valuable data for clinical images in the surveyed papers. The survey concludes that some very significant sophisticated deep learning methods were employed in some studies to grade KOA, which may also work on grading other diseases. Moreover, we show that applying Vision Transformer (ViT) for this specific task can be a better option than most of the convolutional neural networks (CNNs) based models.
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Akila, S.M., Imanov, E., Almezhghwi, K. (2023). Analysis of Knee Osteoarthritis Grading Using Deep Learning. In: Aliev, R.A., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M.B., Sadikoglu, F. (eds) 15th International Conference on Applications of Fuzzy Systems, Soft Computing and Artificial Intelligence Tools – ICAFS-2022. ICAFS 2022. Lecture Notes in Networks and Systems, vol 610. Springer, Cham. https://doi.org/10.1007/978-3-031-25252-5_58
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