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Osteoarthritis Detection Using Densely Connected Neural Network

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1576))

Abstract

Osteoarthritis (OA) is progressive deterioration kind of bone joint disease. Knee OA is most common type of OA, which affect locomotion. Pain in joint, swelling, stiffness and trouble in walking happens to be foremost symptoms of knee OA. KOA detection by medical practitioner is most commonly done with the help of radiographs. The radiographic findings in knee OA are joint space narrowing, formation of bone spurs and increased bone density. In this study, we bring out a method to detect knee OA using knee x-ray images, implemented in MATLAB. The detection is based on clinical changes that happen in bone ends. Nine hundred and fifty knee radiographic images in antero-posterior projection are used, collected from Osteoarthritis Initiative dataset. Those images are used to train Dense Net Neural Network model. Feature maps are computed for the given X-ray images to classify into osteoarthritic knee or normal knee. The trained model gives the validation accuracy of 95.56%. These results indicate the proposed methodology can be used by medical practitioner for assessment of OA.

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References

  1. Anifah, L., Purnama, I.K.E., Hariadi, M., Purnomo, M.H.: Automatic segmentation of impaired joint space area for osteoarthritis knee on X-ray image using gabor filter based morphology process. IPTEK J. Technol. Sci. 22(3) (2011)

    Google Scholar 

  2. Arthritis of the Knee - Orthoinfo - AAOS. OrthoInfo (n.d.). https://orthoinfo.aaos.org/en/diseases-conditions/arthritis-of-the-knee/. Accessed Feb 2021

  3. Bandyopadhyay, S.K.: An edge detection algorithm for human knee osteoarthritis images. J. Glob. Res. Comput. Sci. 2(2) (2011)

    Google Scholar 

  4. Bindushree, R., Kubakaddi, S., Urs, N.: Detection of knee osteoarthritis by measuring the joint space width in knee X-ray images. Int. J. Electron. Commun. 3, 18–21 (2015)

    Google Scholar 

  5. Brahim, A., et al.: A decision support tool for early detection of knee osteoarthritis using X-ray imaging and Machine Learning: data from the osteoarthritis initiative. Comput. Med. Imaging Graph. 73, 11–18 (2019). https://doi.org/10.1016/j.compmedimag.2019.01.007

    Article  Google Scholar 

  6. Chen, P.: Knee Osteoarthritis Severity Grading Dataset. Mendeley Data, V1 (2018). https://doi.org/10.17632/56rmx5bjcr.1

  7. Gornale, S.S., Patravali, P.U., Hiremath, P.S.: Osteoarthritis detection in knee radiographic images using multiresolution wavelet filters. In: Santosh, K.C., Gawali, B. (eds.) RTIP2R 2020. CCIS, vol. 1381, pp. 36–49. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-0493-5_4

    Chapter  Google Scholar 

  8. Gornale, S.S., Patravali, P.U., Manza, R.R.: A survey on exploration and classification of osteoarthritis using image processing techniques. Int. J. Sci. Eng. Res. 7(6), 334–355 (2016)

    Google Scholar 

  9. Lee, H.C., Lee, J.S., Lin, M.C.J., Wu, C.H., Sun, Y.N.: Automatic assessment of knee osteoarthritis parameters from two-dimensional X-ray image. In: First International Conference on Innovative Computing, Information and Control-Volume I (ICICIC 2006), vol. 2, pp. 673–676. IEEE, August 2006

    Google Scholar 

  10. Liu, B., Luo, J., Huang, H.: Toward automatic quantification of knee osteoarthritis severity using improved Faster R-CNN. Int. J. Comput. Assist. Radiol. Surg. 15(3), 457–466 (2020). https://doi.org/10.1007/s11548-019-02096-9

    Article  Google Scholar 

  11. Mahmood, N., Shah, A., Waqas, A., Abubakar, A., Kamran, S., Zaidi, S.B.: Image segmentation methods and edge detection: an application to knee joint articular cartilage edge detection. J. Theor. Appl. Inf. Tech. 71(1), 87–96 (2015)

    Google Scholar 

  12. Norman, B., Pedoia, V., Noworolski, A., Link, T.M., Majumdar, S.: Applying densely connected convolutional neural networks for staging osteoarthritis severity from plain radiographs. J. Digit. Imaging 32(3), 471–477 (2019)

    Article  Google Scholar 

  13. Pandey, M.S., Rajitha, B., Agarwal, S.: Computer assisted automated detection of knee osteoarthritis using X-ray images. Sci. Technol. 1(2), 74–79 (2015)

    Google Scholar 

  14. Pratiwi, D., Santika, D.D., Pardamean, B.: An application of backpropagation artificial neural network method for measuring the severity of Osteoarthritis. arXiv preprint arXiv:1309.7522 (2013)

  15. Wahyuningrum, R.T., Anifah, L., Eddy Purnama, I.K., Hery Purnomo, M.: A new approach to classify knee osteoarthritis severity from radiographic images based on CNN-LSTM method. In: 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), pp. 1–6 (2019). https://doi.org/10.1109/ICAwST.2019.8923284

  16. Shaikh, M.H., Panbude, S., Joshi, A.: Image segmentation techniques and its applications for knee joints: a survey. IOSR J. Electron. Commun. Eng. (IOSR-JECE) 9(5), 23–28 (2014)

    Article  Google Scholar 

  17. Shamir, L., et al.: Knee X-ray image analysis method for automated detection of osteoarthritis. IEEE Trans. Biomed. Eng. 56(2), 407–415 (2008)

    Article  Google Scholar 

  18. Shan, L., Zach, C., Charles, C., Niethammer, M.: Automatic atlas-based three-label cartilage segmentation from MR knee images. Med. Image Anal. 18(7), 1233–1246 (2014)

    Article  Google Scholar 

  19. Sharma, P., Singh, J.M.: A novel approach towards X-ray bone image segmentation using discrete step algorithm. Int. J. Emerg. Trends Technol. Comput. Sci. 2(5), 191–195 (2013)

    Google Scholar 

  20. Subramoniam, B.: A non-invasive computer aided diagnosis of osteoarthritis from digital X-ray images 2015

    Google Scholar 

  21. Wagaj, B.L., Patil, M.M.: Osteoarthritis disease detection with the help of image processing technique. Int. J. Comput. Appl. 975, 8887 (2015)

    Google Scholar 

  22. Wittenauer, R., Smith, L., Aden, K.: Background Paper 6.12 Osteoarthritis. World Health Organisation (2013)

    Google Scholar 

  23. Yu, C., Zhao, B., Li, Y., Zang, H., Li, L.: Vibrational spectroscopy in assessment of early osteoarthritis–a narrative review. Int. J. Mol. Sci. 22, 5235 (2021). https://doi.org/10.3390/ijms22105235

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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 Chaugule & V. S. Malemath

Authors
  1. Sushma Chaugule
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  2. V. S. Malemath
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Correspondence to Sushma Chaugule .

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Editors and Affiliations

  1. University of South Dakota, Vermillion, SD, USA

    KC Santosh

  2. Central University of Karnataka, Gulbarga, India

    Ravindra Hegadi

  3. Indian Statistical Institute, Kolkata, India

    Umapada Pal

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Chaugule, S., Malemath, V.S. (2022). Osteoarthritis Detection Using Densely Connected Neural Network. In: Santosh, K., Hegadi, R., Pal, U. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2021. Communications in Computer and Information Science, vol 1576. Springer, Cham. https://doi.org/10.1007/978-3-031-07005-1_9

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  • DOI: https://doi.org/10.1007/978-3-031-07005-1_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07004-4

  • Online ISBN: 978-3-031-07005-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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