SpringerLink
Log in

Classifying Humerus Fracture Using X-Ray Images

  • Conference paper
  • First Online:
The Fourth Industrial Revolution and Beyond

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 980))

Abstract

Bone is the most important part of our body which holds the whole structure of human body. The long bone situated in the upper arm of human body between the shoulder and elbow junction is known as “Humerus”. Humerus works as a structural support of the muscles and arms in the upper body which helps in the movement of the hand and elbow. Therefore, any fracture in humerus disrupts our daily lives. The manual fracture detection process where the doctors detect the fracture by analyzing X-ray images is quite time consuming and also error prone. Therefore, we have introduced an automated system to diagnose humerus fracture in an efficient way. In this study, we have focused on deep learning algorithm for fracture detection. In this purpose at first, 1266 X-ray images of humerus bone including fractured and non-fractured have been collected from a publicly available dataset called “MURA”. As a deep learning model has been used here, data augmentation has been applied to increase the dataset for reducing over-fitting problem. Finally, all the images are passed through CNN model to train the images and classify the fractured and non-fractured bone. Moreover, different pretrained model has also been applied in our dataset to find out the best accuracy. After implementation, it is observed that our model shows the best accuracy which is 80% training accuracy and 78% testing accuracy comparing with other models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Thailand)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 139.09
Price includes VAT (Thailand)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 169.99
Price excludes VAT (Thailand)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
EUR 169.99
Price excludes VAT (Thailand)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Adams, M., Chen, W., Holcdorf, D., McCusker, M.W., Howe, P.D., Gaillard, F.: Computer vs human: deep learning versus perceptual training for the detection of neck of femur fractures. J. Med. Imaging Radiat. Oncol. 63(1), 27–32 (2019)

    Article  Google Scholar 

  2. Ahmed, T.U., Hossain, S., Hossain, M.S., ul Islam, R., Andersson, K.: Facial expression recognition using convolutional neural network with data augmentation. In: 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pp. 336–341. IEEE (2019)

    Google Scholar 

  3. Basnin, N., Nahar, L., Hossain, M.S.: An integrated CNN-LSTM model for micro hand gesture recognition. In: International Conference on Intelligent Computing & Optimization, pp. 379–392. Springer, Berlin (2020)

    Google Scholar 

  4. Basnin, N., Sumi, T.A., Hossain, M.S., Andersson, K.: Early detection of Parkinson’s disease from micrographic static hand drawings. In: International Conference on Brain Informatics, pp. 433–447. Springer, Berlin (2021)

    Google Scholar 

  5. Chung, S.W., Han, S.S., Lee, J.W., Oh, K.S., Kim, N.R., Yoon, J.P., Kim, J.Y., Moon, S.H., Kwon, J., Lee, H.J., et al.: Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop. 89(4), 468–473 (2018)

    Article  Google Scholar 

  6. De Vries, B., Hegeman, J., Nijmeijer, W., Geerdink, J., Seifert, C., Groothuis-Oudshoorn, C.: Comparing three machine learning approaches to design a risk assessment tool for future fractures: predicting a subsequent major osteoporotic fracture in fracture patients with osteopenia and osteoporosis. Osteoporos. Int. 32(3), 437–449 (2021)

    Article  Google Scholar 

  7. Demir, S., Key, S., Tuncer, T., Dogan, S.: An exemplar pyramid feature extraction based humerus fracture classification method. Med. Hypotheses 140, 109663 (2020)

    Article  Google Scholar 

  8. Devana, S.K., Shah, A.A., Lee, C., Gudapati, V., Jensen, A.R., Cheung, E., Solorzano, C., van der Schaar, M., SooHoo, N.F.: Development of a machine learning algorithm for prediction of complications and unplanned readmission following reverse total shoulder arthroplasty. J. Shoulder Elbow Arthroplasty 5, 24715492211038172 (2021)

    Article  Google Scholar 

  9. Islam, M.Z., Hossain, M.S., ul Islam, R., Andersson, K.: Static hand gesture recognition using convolutional neural network with data augmentation. In: 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pp. 324–329. IEEE (2019)

    Google Scholar 

  10. Islam, R.U., Hossain, M.S., Andersson, K.: A deep learning inspired belief rule-based expert system. IEEE Access 8, 190637–190651 (2020)

    Article  Google Scholar 

  11. Jamil, M.N., Hossain, M.S., ul Islam, R., Andersson, K.: A belief rule based expert system for evaluating technological innovation capability of high-tech firms under uncertainty. In: 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pp. 330–335. IEEE (2019)

    Google Scholar 

  12. Kabir, S., Islam, R.U., Hossain, M.S., Andersson, K.: An integrated approach of belief rule base and deep learning to predict air pollution. Sensors 20(7), 1956 (2020)

    Article  Google Scholar 

  13. Karim, R., Andersson, K., Hossain, M.S., Uddin, M.J., Meah, M.P.: A belief rule based expert system to assess clinical bronchopneumonia suspicion. In: 2016 Future Technologies Conference (FTC), pp. 655–660. IEEE (2016)

    Google Scholar 

  14. Negrillo-Cárdenas, J., Jiménez-Pérez, J.R., Cañada-Oya, H., Feito, F.R., Delgado-Martínez, A.D.: Automatic detection of landmarks for the analysis of a reduction of supracondylar fractures of the humerus. Med. Image Anal. 64, 101729 (2020)

    Article  Google Scholar 

  15. Negrillo-Cárdenas, J., Jiménez-Pérez, J.R., Feito, F.R.: Automatic detection of distal humerus features: first steps. In: VISIGRAPP (1: GRAPP), pp. 354–359 (2019)

    Google Scholar 

  16. Pranata, Y.D., Wang, K.C., Wang, J.C., Idram, I., Lai, J.Y., Liu, J.W., Hsieh, I.H.: Deep learning and surf for automated classification and detection of calcaneus fractures in CT images. Comput. Methods Programs Biomed. 171, 27–37 (2019)

    Article  Google Scholar 

  17. Progga, N.I., Hossain, M.S., Andersson, K.: A deep transfer learning approach to diagnose covid-19 using X-ray images. In: 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), pp. 177–182. IEEE (2020)

    Google Scholar 

  18. Rahaman, S., Hossain, M.S.: A belief rule based clinical decision support system to assess suspicion of heart failure from signs, symptoms and risk factors. In: 2013 International Conference on Informatics, Electronics and Vision (ICIEV), pp. 1–6. IEEE (2013)

    Google Scholar 

  19. Rezaoana, N., Hossain, M.S., Andersson, K.: Detection and classification of skin cancer by using a parallel CNN model. In: 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), pp. 380–386. IEEE (2020)

    Google Scholar 

  20. Sasidhar, A., Thanabal, M., Ramya, P.: Efficient transfer learning model for humerus bone fracture detection. Ann. Rom. Soc. Cell Biol. 3932–3942 (2021)

    Google Scholar 

  21. Sezer, A., Sezer, H.B.: Convolutional neural network based diagnosis of bone pathologies of proximal humerus. Neurocomputing 392, 124–131 (2020)

    Article  Google Scholar 

  22. Shrader, M.W., Sanchez-Sotelo, J., Sperling, J.W., Rowland, C.M., Cofield, R.H.: Understanding proximal humerus fractures: image analysis, classification, and treatment. J. Shoulder Elbow Surgery 14(5), 497–505 (2005)

    Article  Google Scholar 

  23. Sumi, T.A., Hossain, M.S., Islam, R.U., Andersson, K.: Human gender detection from facial images using convolution neural network. In: International Conference on Applied Intelligence and Informatics, pp. 188–203. Springer, Berlin (2021)

    Google Scholar 

  24. Uddin Ahmed, T., Jamil, M.N., Hossain, M.S., Andersson, K., Hossain, M.S.: An integrated real-time deep learning and belief rule base intelligent system to assess facial expression under uncertainty. In: 9th International Conference on Informatics, Electronics & Vision (ICIEV). IEEE Computer Society (2020)

    Google Scholar 

  25. Zisad, S.N., Chowdhury, E., Hossain, M.S., Islam, R.U., Andersson, K.: An integrated deep learning and belief rule-based expert system for visual sentiment analysis under uncertainty. Algorithms 14(7), 213 (2021)

    Article  Google Scholar 

  26. Zisad, S.N., Hossain, M.S., Andersson, K.: Speech emotion recognition in neurological disorders using convolutional neural network. In: International Conference on Brain Informatics, pp. 287–296. Springer, Berlin (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Chittagong, Chittagong, Bangladesh

    Tahmina Akter Sumi & Md. Shahadat Hossain

  2. International Islamic University Chittagong, Sonaichhari, Bangladesh

    Nanziba Basnin

  3. Lulea University of Technology, Skelleftea, Sweden

    Karl Andersson

  4. University of Liberal Arts Bangladesh, Dhaka, 1209, Bangladesh

    Md. Sazzad Hoassain

Authors
  1. Tahmina Akter Sumi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nanziba Basnin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Md. Shahadat Hossain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Karl Andersson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Md. Sazzad Hoassain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Md. Shahadat Hossain .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University Grants Commission of Banglade, Dhaka, Bangladesh

    Md. Sazzad Hossain

  2. Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

    Satya Prasad Majumder

  3. School of Computing, Engineering, and Intelligent Systems, University of Ulster, Londonderry, UK

    Nazmul Siddique

  4. Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh

    Md. Shahadat Hossain

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sumi, T.A., Basnin, N., Hossain, M.S., Andersson, K., Hoassain, M.S. (2023). Classifying Humerus Fracture Using X-Ray Images. In: Hossain, M.S., Majumder, S.P., Siddique, N., Hossain, M.S. (eds) The Fourth Industrial Revolution and Beyond. Lecture Notes in Electrical Engineering, vol 980. Springer, Singapore. https://doi.org/10.1007/978-981-19-8032-9_37

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-8032-9_37

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8031-2

  • Online ISBN: 978-981-19-8032-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation