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Knee Osteoarthritis Grading Using DenseNet and Radiographic Images

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Abstract

Osteoarthritis (OA) is the constant dilapidation of the bone joint. Knee OA is most typical, which affects mobility. Joint pain, swelling, stiffness, and strenuous walking are major indications of knee OA. Radiographs of affected joints are the prime way to identify OA, which helps discover joint space narrowing, bone spurs development, and increased bone density. In this paper, we present a method to detect knee OA severity hinged on KL grading, implemented in MATLAB. Knee radiographic images from the OAI dataset are used to train the DenseNet, a type of Convolutional Neural Network. Every layer has access to its preceding feature maps called collective knowledge, and every layer adds information to this collective knowledge that aids in better and accurate classification into Grade 0 through Grade 4. This model outperforms the existing models and indicates that DenseNet is an efficient CNN and helps medical practitioners with a better way to diagnose knee OA severity.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, KLE DR. M. S. Sheshgiri College of Engineering and Technology, Belagavi, India

    Sushma V. Chaugule & V. S. Malemath

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  1. Sushma V. Chaugule
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  2. V. S. Malemath
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This article is part of the topical collection “Advances in Applied Image Processing and Pattern Recognition” guest-edited by K C Santosh.

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Chaugule, S., Malemath, V.S. Knee Osteoarthritis Grading Using DenseNet and Radiographic Images. SN COMPUT. SCI. 4, 63 (2023). https://doi.org/10.1007/s42979-022-01468-4

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