Open Access

ARTICLE

Detection Algorithm of Knee Osteoarthritis Based on Magnetic Resonance Images

by Xin Wang^*, Shuang Liu, Chang-Cai Zhou

College of Computers Science and Engineering, Changchun University of Technology, Changchun, 130000, China

* Corresponding Author: Xin Wang. Email:

Intelligent Automation & Soft Computing 2023, 37(1), 221-234. https://doi.org/10.32604/iasc.2023.036766

Received 11 October 2022; Accepted 06 January 2023; Issue published 29 April 2023

Abstract

Knee osteoarthritis (OA) is a common disease that impairs knee function and causes pain. Currently, studies on the detection of knee OA mainly focus on X-ray images, but X-ray images are insensitive to the changes in knee OA in the early stage. Since magnetic resonance (MR) imaging can observe the early features of knee OA, the knee OA detection algorithm based on MR image is innovatively proposed to judge whether knee OA is suffered. Firstly, the knee MR images are preprocessed before training, including a region of interest clipping, slice selection, and data augmentation. Then the data set was divided by patient-level and the knee OA was classified by the deep transfer learning method based on the DenseNet201 model. The method divides the training process into two stages. The first stage freezes all the base layers and only trains the weights of the embedding neural networks. The second stage unfreezes part of the base layers and trains the unfrozen base layers and the weights of the embedding neural network. In this step, we design a block-by-block fine-tuning strategy for training based on the dense blocks, which improves detection accuracy. We have conducted training experiments with different depth modules, and the experimental results show that gradually adding more dense blocks in the fine-tuning can make the model obtain better detection performance than only training the embedded neural network layer. We achieve an accuracy of 0.921, a sensitivity of 0.960, a precision of 0.885, a specificity of 0.891, an F1-Score of 0.912, and an MCC of 0.836. The comparative experimental results on the OAI-ZIB dataset show that the proposed method outperforms the other detection methods with the accuracy of 92.1%.

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