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An Extreme Learning Machine-Based AutoEncoder (ELM-AE) for Denoising Knee X-ray Images and Grading Knee Osteoarthritis Severity

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1704))

Abstract

Osteoarthritis (OA) is the most usual form of arthritis. Radiologists assess the OA severity by observing the pieces of evidence on both sides of knee bones, hinged on the Kellgren–Lawrence (KL) grading system. Computer-assisted diagnosis has been a prime field of research for the past few decades as it tends to provide highly accurate performance. In this work, we propose the Knee Osteoarthritis (KOA) classification problem to segregate the severity into five grades. The proposed work can be framed into two-stage, using X-ray images. Stage one deals with preprocessing and denoising, while stage two deals with classification. This work considers, a standard OAI dataset as well as locally collected images as input, and are fed to an Extreme Learning Machine-based AutoEncoder (ELM-AE) to get the denoised images, which are then used for training the Dense Neural Network model DenseNet201and are later classified, based on KL grades. In experimentation, evaluation of performance is carried out for the model with and without using autoencoders. It is observed that with autoencoders the overall performance is enhanced significantly for standard as well as the local dataset.

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References

  1. Hayes, B., Kittelson, A., Loyd, B., Wellsandt, E., Flug, J., Stevens-Lapsley, J.: Assessing radiographic knee osteoarthritis: an online training tutorial for the Kellgren-Lawrence grading scale. MedEdPORTAL (2016). https://doi.org/10.15766/mep_2374-8265.10503

    Article  Google Scholar 

  2. Kohn, M.D., Sassoon, A.A., Fernando, N.D.: Classifications in brief: Kellgren-Lawrence classification of osteoarthritis. Clin. Orthop. Relat. Res. 474(8), 1886–1893 (2016). https://doi.org/10.1007/s11999-016-4732-4

    Article  Google Scholar 

  3. Kellgren, J.H., Lawrence, J.S.: Radiological assessment of osteo-arthrosis. Ann. Rheum. Dis. 16(4), 494–502 (1957). https://doi.org/10.1136/ard.16.4.494

    Article  Google Scholar 

  4. Chen, P., Gao, L., Shi, X., Allen, K., Yang, L.: Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss. Comput. Med. Imaging Graph. 75, 84–92 (2019). https://doi.org/10.1016/j.compmedimag.2019.06.002

    Article  Google Scholar 

  5. Dongare, P.P., Gornale, S.S.: Medical Imaging in Clinical Applications Algorithmic and Computer Based.pdf, no. May (2021)

    Google Scholar 

  6. Hayashi, D., Roemer, F.W., Guermazi, A.: Imaging of osteoarthritis - recent research developments and future perspective. Br. J. Radiol. 91(1085), 20170349 (2018). https://doi.org/10.1259/bjr.20170349

    Article  Google Scholar 

  7. Tiulpin, A., Thevenot, J., Rahtu, E., Lehenkari, P., Saarakkala, S.: Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Sci. Rep. 8(1), 1 (2018). https://doi.org/10.1038/s41598-018-20132-7

    Article  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, 334–355 (2016)

    Google Scholar 

  9. Ruikar, D.D., Hegadi, R.S., Santosh, K.C.: A systematic review on orthopedic simulators for psycho-motor skill and surgical procedure training. J. Med. Syst. 42(9), 1–21 (2018). https://doi.org/10.1007/s10916-018-1019-1

    Article  Google Scholar 

  10. Ruikar, D.D., Sawat, D.D., Santosh, K.C.: A systematic review of 3D imaging in biomedical applications. In: Medical Imaging. Boca Raton: Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc, 2020, pp. 154–181. CRC Press (2019). https://doi.org/10.1201/9780429029417-8

  11. Gornale, S.S., Patravali, P.U., Manza, R.R.: Detection of osteoarthritis using knee X-ray image analyses: a machine vision based approach. Int. J. Comput. Appl. 145(1), 20–26 (2016). https://doi.org/10.5120/ijca2016910544

    Article  Google Scholar 

  12. Sumathi, S., Paneerselvam, S.: Computational intelligence. In: Computational Intelligence Paradigms, pp. 25–52 (2020). https://doi.org/10.1201/9781439809037-6

  13. Teoh, Y.X., et al.: Discovering knee osteoarthritis imaging features for diagnosis and prognosis: review of manual imaging grading and machine learning approaches. J. Healthc. Eng. 2022 (2022). https://doi.org/10.1155/2022/4138666

  14. Kubakaddi, S., Urs, N.: Detection of knee osteoarthritis by measuring the joint space width in knee X-ray images. Int. J. Electron. Commun. 3(4), 18–21 (2019)

    Google Scholar 

  15. Navale, D.I., Ruikar, D.D., Houde, K.V., Hegadi, R.S.: DWT textural feature-based classification of osteoarthritis using knee X-ray images. In: Santosh, K.C., Gawali, B. (eds.) RTIP2R 2020. CCIS, vol. 1381, pp. 50–59. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-0493-5_5

    Chapter  Google Scholar 

  16. Guida, C., Zhang, M., Shan, J.: Knee osteoarthritis classification using 3D CNN and MRI. Appl. Sci. 11(11), 5196 (2021). https://doi.org/10.3390/app11115196

    Article  Google Scholar 

  17. Schiratti, J.B., et al.: A deep learning method for predicting knee osteoarthritis radiographic progression from MRI. Arthritis Res. Ther. 23(1), 1–10 (2021). https://doi.org/10.1186/s13075-021-02634-4

    Article  Google Scholar 

  18. Singha, R., Dalai, C.K., Sarkar, D.: A study on evaluation of knee osteoarthritis with MRI and comparing it with CT scan, high resolution USG and conventional radiography. Asian J. Med. Sci. 12(12), 120–125 (Dec.2021). https://doi.org/10.3126/ajms.v12i12.39174

    Article  Google Scholar 

  19. Vashishtha, A., Acharya, A.K.: An overview of medical imaging techniques for knee osteoarthritis disease. Biomed. Pharmacol. J. 14(2), 903–919 (2021). https://doi.org/10.13005/bpj/2192

  20. Gondara, L.: Medical image denoising using convolutional denoising autoencoders. In: IEEE International Conference on Data Mining Workshops, ICDMW, pp. 241–246 (2016). https://doi.org/10.1109/ICDMW.2016.0041

  21. Vankayalapati, R., Muddana, A.L.: Denoising of images using deep convolutional autoencoders for brain tumor classification. Rev. d’Intelligence Artif. 35(6), 489–496 (2021). https://doi.org/10.18280/ria.350607

    Article  Google Scholar 

  22. 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 (2006). https://doi.org/10.1109/ICICIC.2006.242

  23. 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 (2018). https://doi.org/10.1007/s10278-018-0098-3

    Article  Google Scholar 

  24. 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 

  25. Zahurul, S., Zahidul, S., Jidin, R.: An adept edge detection algorithm for human knee osteoarthritis images. In: 2010 International Conference on Signal Acquisition and Processing, ICSAP 2010, vol. 2, no. 4, pp. 375–379 (2010). https://doi.org/10.1109/ICSAP.2010.53

  26. 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). https://doi.org/10.12962/j20882033.v22i3.72

  27. Gan, H.S., Sayuti, K.A., Karim, A.H.A., Rosidi, R.A.M., Khaizi, A.S.A.: Analysis on semi-automated knee cartilage segmentation model using inter-observer reproducibility. In: Proceedings of the 7th International Conference on Bioscience, Biochemistry and Bioinformatics - ICBBB 2017, pp. 12–16 (2017). https://doi.org/10.1145/3051166.3051169

  28. Suganyadevi, S., Seethalakshmi, V., Balasamy, K.: A review on deep learning in medical image analysis. Int. J. Multimed. Inf. Retr. 11(1), 19–38 (2022). https://doi.org/10.1007/s13735-021-00218-1

  29. Subramoniam, M., Barani, S., Rajini, V.: A non-invasive computer aided diagnosis of osteoarthritis from digital x-ray images. Biomed. Res. 26(4), 721–729 (2015)

    Google Scholar 

  30. Shamir, L., Ling, S.M., Scott, W., Hochberg, M., Ferrucci, L., Goldberg, I.G.: Early detection of radiographic knee osteoarthritis using computer-aided analysis. Osteoarthr. Cartil. 17(10), 1307–1312 (2009). https://doi.org/10.1016/j.joca.2009.04.010

    Article  Google Scholar 

  31. Gornale, S.S., Patravali, P.U., Hiremath, P.S.: Automatic detection and classification of knee osteoarthritis using Hu’s invariant moments. Front. Robot. AI 7, 591827 (2020). https://doi.org/10.3389/frobt.2020.591827

    Article  Google Scholar 

  32. Shaikh, M.H., Panbude, S., Joshi, A.: Image segmentation techniques and its applications for knee joints: a survey. IOSR J. Electron. Commun. Eng. 9(5), 23–28 (2014). https://doi.org/10.9790/2834-09542328

    Article  Google Scholar 

  33. Pandey, M.S.: Science & Technology, no. April (2015)

    Google Scholar 

  34. Gornale, S.S., Patravali, P.U., Uppin, A.M., Hiremath, P.S.: Study of segmentation techniques for assessment of osteoarthritis in knee X-ray images. Int. J. Image Graph. Signal Process. 11(2), 48–57 (2019). https://doi.org/10.5815/ijigsp.2019.02.06

    Article  Google Scholar 

  35. Ahmed, S.M., Mstafa, R.J.: A comprehensive survey on bone segmentation techniques in knee osteoarthritis research: from conventional methods to deep learning. Diagnostics 12(3), 611 (2022). https://doi.org/10.3390/diagnostics12030611

    Article  Google Scholar 

  36. 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 (Oct.2014). https://doi.org/10.1016/j.media.2014.05.008

    Article  Google Scholar 

  37. Chaugule, S., Malemath, V.S.: Osteoarthritis detection using densely connected neural network. In: Santosh, K., Hegadi, R., Pal, U. (eds.) Recent Trends in Image Processing and Pattern Recognition, pp. 85–92. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-07005-1_9

    Chapter  Google Scholar 

  38. Hegadi, R.S., Navale, D.I., Pawar, T.D., Ruikar, D.D.: Osteoarthritis detection and classification from knee X-ray images based on artificial neural network. In: Santosh, K.C., Hegadi, R.S. (eds.) RTIP2R 2018. CCIS, vol. 1036, pp. 97–105. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-9184-2_8

    Chapter  Google Scholar 

  39. Gornale, S.S., Patravali, P.U., Hiremath, P.S.: Detection of osteoarthritis in knee radiographic images using artificial neural network. Int. J. Innov. Technol. Explor. Eng. 8(12), 2429–2434 (2019). https://doi.org/10.35940/ijitee.L3011.1081219

  40. Hegadi, R.S., Navale, D.N., Pawar, T.D., Ruikar, D.D.: Multi-feature-based classification of osteoarthritis in knee joint X-ray images. In: Medical Imaging. Boca Raton: Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc, 2020, pp. 74–96. CRC Press (2019). https://doi.org/10.1201/9780429029417-5

  41. Jean De Dieu, U., et al.: Diagnosing knee osteoarthritis using artificial neural networks and deep learning. Biomed. Stat. Informatics 2(3), 95–102 (2017). https://doi.org/10.11648/j.bsi.20170203.11

    Article  Google Scholar 

  42. Mahum, R., et al.: A novel hybrid approach based on deep CNN features to detect knee osteoarthritis. Sensors 21(18), 6189 (2021). https://doi.org/10.3390/s21186189

    Article  Google Scholar 

  43. Karim, M.R., et al.: DeepKneeExplainer: explainable knee osteoarthritis diagnosis from radiographs and magnetic resonance imaging. IEEE Access 9, 39757–39780 (2021). https://doi.org/10.1109/ACCESS.2021.3062493

    Article  Google Scholar 

  44. Chen, P.: Knee osteoarthritis severity grading dataset. Mendeley Data, vol. V1 (2018). https://doi.org/10.17632/56rmx5bjcr.1

  45. Hammersberg, P., Stenström, M., Hedtjärn, H., Mångård, M.: Image noise in X-ray imaging caused by radiation scattering and source leakage, a qualitative and quantitative analysis. J. Xray. Sci. Technol. 8(1), 19–29 (1998). http://www.ncbi.nlm.nih.gov/pubmed/22388424

  46. Sevinc, O., Mehrubeoglu, M., Guzel, M.S., Askerzade, I.: An effective medical image classification: transfer learning enhanced by auto encoder and classified with SVM. Trait. du Signal 39(1), 125–131 (2022). https://doi.org/10.18280/ts.390112

    Article  Google Scholar 

  47. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006). https://doi.org/10.1016/j.neucom.2005.12.126

    Article  Google Scholar 

  48. Du, J., Vong, C.M., Chen, C., Liu, P., Liu, Z.: Supervised extreme learning machine-based auto-encoder for discriminative feature learning. IEEE Access 8, 11700–11709 (2020). https://doi.org/10.1109/ACCESS.2019.2962067

    Article  Google Scholar 

  49. Nishio, M., et al.: Convolutional auto-encoders for image denoising of ultra-low-dose CT. Heliyon 3(8), e00393 (2017). https://doi.org/10.1016/j.heliyon.2017.e00393

    Article  Google Scholar 

  50. Huang, G., Liu, Z., Pleiss, G., Van Der Maaten, L., Weinberger, K.: Convolutional networks with dense connectivity. IEEE Trans. Pattern Anal. Mach. Intell. 1 (2019). https://doi.org/10.1109/tpami.2019.2918284

  51. Villa-Pulgarin, J.P., et al.: Optimized convolutional neural network models for skin lesion classification. Comput. Mater. Contin. 70(2), 2131–2148 (2022). https://doi.org/10.32604/cmc.2022.019529

    Article  Google Scholar 

  52. Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V., Kaur, M.: Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning. J. Biomol. Struct. Dyn. 39(15), 5682–5689 (2021). https://doi.org/10.1080/07391102.2020.1788642

    Article  Google Scholar 

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Authors and Affiliations

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

    Sushma Chaugule & V. S. Malemath

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  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. Texas A&M University, College Station, TX, USA

    Ayush Goyal

  3. University of Luxembourg, Luxembourg, Luxembourg

    Djamila Aouada

  4. University of Derby, Derby, UK

    Aaisha Makkar

  5. University of Minnesota, Minneapolis, MN, USA

    Yao-Yi Chiang

  6. IIIT Allahabad, Allahabad, India

    Satish K Singh

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Chaugule, S., Malemath, V.S. (2023). An Extreme Learning Machine-Based AutoEncoder (ELM-AE) for Denoising Knee X-ray Images and Grading Knee Osteoarthritis Severity. In: Santosh, K., Goyal, A., Aouada, D., Makkar, A., Chiang, YY., Singh, S.K. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2022. Communications in Computer and Information Science, vol 1704. Springer, Cham. https://doi.org/10.1007/978-3-031-23599-3_12

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  • DOI: https://doi.org/10.1007/978-3-031-23599-3_12

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