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Grading of Knee Osteoarthritis Using Convolutional Neural Networks

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Abstract

Knee osteoarthritis (OA) is a disease of the joints and a leading cause of disability among the elderly. If detected at an early stage, its advancement can be slowed and the patient’s suffering can be reduced. A new approach involving multiscale convolutional blocks in convolutional neural network (MCBCNN) has been introduced in this paper for automatic classification and grading of knee OA. The proposed model is implemented using pretrained convolutional neural networks (CNNs) and multiscale convolutional filters. Three pretrained CNN models, namely mobileNet2, resNet50 and inceptionNetv3 have been used for the implementation of MCBCNN. Exhaustive performance analysis has been conducted on the three proposed models. The results of knee OA grading delivered by all the three proposed MCBCNNs have been compared. The results show that the performance of MCBCNNs is better than that of the pretrained CNNs. Among the proposed three MCBCNNs, the MCB resNet50 delivers better performance in terms of average accuracy of over \(95\%\), area under curve of nearly 0.9 and F1 score of 0.8.

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Acknowledgements

Authors acknowledge with gratitude the support received from REVA University, Bengaluru, and M S Ramaiah University of Applied Sciences, Bengaluru. Authors also thank profusely the anonymous reviewers of this paper for their constructive criticism.

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Authors and Affiliations

  1. REVA University, Bengaluru, India

    D. R. Sarvamangala

  2. M S Ramaiah University of Applied Sciences, Bengaluru, India

    Raghavendra V. Kulkarni

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  1. D. R. Sarvamangala
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  2. Raghavendra V. Kulkarni
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Correspondence to D. R. Sarvamangala.

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Sarvamangala, D.R., Kulkarni, R.V. Grading of Knee Osteoarthritis Using Convolutional Neural Networks. Neural Process Lett 53, 2985–3009 (2021). https://doi.org/10.1007/s11063-021-10529-3

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