Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

Classification of Uterine Fibroids in Ultrasound Images Using Deep Learning Model

  • Conference paper
  • First Online:

Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 13352))

Included in the following conference series:

Abstract

An abnormal growth develop in female uterus is uterus fibroids. Sometimes these fibroids may cause severe problems like miscarriage. If this fibroids are not detected it ultimately grows in size and numbers. Among different image modalities, ultrasound is more efficient to detect uterus fibroids. This paper proposes a model in deep learning for fibroid detection with many advantages. The proposed deep learning model overpowers the drawbacks of the existing methodologies of fibroid detection in all stages like noise removal, contrast enhancement, Classification. The preprocessed image is classified into two classes of data: fibroid and non-fibroid, which is done using the MBF-CDNN method. The method is validated using the parameters Sensitivity, specificity, accuracy, precision, F-measure. It is found that the sensitivity is 94.44%, specificity 95% and accuracy 94.736%.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /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
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 12583
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 15729
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Similar content being viewed by others

References

  1. Sriraam, N., Nithyashri, D., Vinodashri, L., Niranjan, P.M.: Detection of uterine fibroids using wavelet packet features with BPNN classifier. In: IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), pp. 406–409 (2010)

    Google Scholar 

  2. Yuan, Y., Hoogi, A., Beaulieu, C.F., Meng, M.Q.H., Rubin, D.L.: Weighted locality–constrained linear coding for lesson classification in CT images. In: Proceedings of 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2015)

    Google Scholar 

  3. Rundo, L., et al.: Combining split-and-merge and multi-seed region growing algorithms for uterine fibroid segmentatiogn in MRgFUS treatments. Med. Biol. Eng. Comput.54(7), 1071–84 (2016)

    Google Scholar 

  4. Ni, B., He, F., Yuan, Z.: Segmentation of uterine fibroid ultrasound images using a dynamicstatistical shape model in HIFU therapy. Comput. Med. Imaging Graph.46, 302–314 (2015)

    Google Scholar 

  5. Fallahi, A., Pooyan, M., Khotanlou, H., Hashemi, H., Firouznia, K., Oghabian, M.A.: Uterine fibroid segmentation on Multiplan MRI using FCM, MPFCM and morphological operations. Biomed. Eng. Appl. Basis Commun.26(1) (2014)

    Google Scholar 

  6. Jeyalakshmi, R., Kadarkarai, R.: Segmentation and feature ex traction of fluid-filled uterine fibroid-a knowledge-based approach. Int. J. Sci. Technol.4, 405–416 (2010). ISSN 1905-7873

    Google Scholar 

  7. Harlapur, S.K., Hegadi, R.S.: Segmentation and analysis of fibroid from ultrasound images. Int. J. Comput. Appl.0975, 8887 (2015)

    Google Scholar 

  8. Yao, J., Chen, D., Lu, W., Premkumar, A.: Uterine fibroid segmentation and volume measurement on MRI. In: Proceedings of the SPIE, vol. 6143 (2006)

    Google Scholar 

  9. Alush, A., Greenspan, H., Goldberger, J.: Automated and interactive lesion detection and segmentation in uterine cervix images. IEEE Trans. Med. Imaging29(2) (2010)

    Google Scholar 

  10. Padghamod, M.J., Gawande, J.P.: Classification of ultrasonic uterine images. Adv. Res. Electr. Electron. Eng.1(3), 89–92 (2014)

    Google Scholar 

  11. Prabakar, S., Porkumaran, K., Guna Sundari, J.: Uterine fibroid segmentation and measurement based on morphological functions in graphical vision assistant tool. In: Sridhar, V., Sheshadri, H., Padma, M. (eds.) Emerging Research in Electronics, Computer Science and Technology. LNEE, vol. 248, pp. 357–366. Springer, New Delhi (2014).https://doi.org/10.1007/978-81-322-1157-0_36

  12. Ghetas, M.: Learning-based monarch butterfly optimization algorithm for solving numerical optimization problems. Neural Comput. Appl.34, 3939–3957 (2022)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India

    K. T. Dilna, J. Anitha & D. Jude Hemanth

  2. Department of ECE, College of Engineering and Technology, Payyanur, India

    K. T. Dilna

  3. School of Computer Science and Engineering, University of Westminster, London, UK

    A. Angelopoulou & T. Chaussalet

  4. School of Physics, Engineering and Computer Science, University of Hertfordshire, Herts, UK

    E. Kapetanios

Authors
  1. K. T. Dilna

    You can also search for this author inPubMed Google Scholar

  2. J. Anitha

    You can also search for this author inPubMed Google Scholar

  3. A. Angelopoulou

    You can also search for this author inPubMed Google Scholar

  4. E. Kapetanios

    You can also search for this author inPubMed Google Scholar

  5. T. Chaussalet

    You can also search for this author inPubMed Google Scholar

  6. D. Jude Hemanth

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toD. Jude Hemanth.

Editor information

Editors and Affiliations

  1. Brunel University London, London, UK

    Derek Groen

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

  3. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

Rights and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dilna, K.T., Anitha, J., Angelopoulou, A., Kapetanios, E., Chaussalet, T., Hemanth, D.J. (2022). Classification of Uterine Fibroids in Ultrasound Images Using Deep Learning Model. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13352. Springer, Cham. https://doi.org/10.1007/978-3-031-08757-8_5

Download citation

Publish with us

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /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
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 12583
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 15729
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

[8]ページ先頭
©2009-2025 Movatter.jp