Skip to main content

Detection of Liver Tumor Candidates from CT Images Using Deep Convolutional Neural Networks

  • Conference paper
  • First Online:
Innovation in Medicine and Healthcare 2017 (KES-InMed 2018 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 71))

Included in the following conference series:


There are multiple types of tumors occurring in the liver. Different tumors have different visual appearance and their visual appearance changes after injection of the contrast medium. So detection of liver tumors is considered as a challenging task. In this paper, we propose a method for detection of liver tumor candidates from CT images using a deep convolutional neural network. Experimental results show that we can significantly improve the detection accuracy by using our proposed method compared with the previous researches.

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

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

Similar content being viewed by others


  1. National Cancer Center, Japan: Center for Cancer Control and Information Services.

  2. Ramaraju, P.V., et al.: Feature based detection of liver tumor using K-means clustering and classifying using probabilistic neural networks. Int. J. Eng. Comput. Sci. 4, 11910–11915 (2015)

    Google Scholar 

  3. Ali, A.H., et al.: Diagnosis of liver tumor from CT images using first order statistical features. Int. J. Eng. Trends Technol. 20, 155–158 (2015)

    Article  Google Scholar 

  4. Mala, K., et al.: Neural network based texture analysis of liver tumor from computed tomography images. Int. J. Biomed. Sci. 2, 33–40 (2006)

    Google Scholar 

  5. Park, S.-J., et al.: Automatic hepatic tumor segmentation using statistical optimal threshold. In: Computational Science – ICCS 2005, vol. 3514, pp. 934–940. Springer, Heidelberg (2005)

    Google Scholar 

  6. Masuda, Y., et al.: Liver tumor detection in CT images by adaptive contrast enhancement and the EM/MPM algorithm. In: Proceedings of IEEE International Conference on Image Processing (ICIP2013), pp. 1453–1456 (2011)

    Google Scholar 

  7. Foruzan, A.H., Chen, Y.-W.: Improved segmentation of low-contrast lesions using sigmoid edge model. Int. J. CARS 11, 1267–1283 (2016)

    Article  Google Scholar 

  8. Konno, Y., et al.: Bayesian model for liver tumor enhancement. In: Chen, Y.-W., et al. (eds.) Innovation in Medicine and Healthcare 2016, pp. 227–235. Springer (2016)

    Google Scholar 

  9. Dong, C., et al.: Simultaneous segmentation of multiple organs using random walks. J. Inf. Process. Soc. Japan 24(2), 320–329 (2016)

    Google Scholar 

  10. Dong, C., et al.: Segmentation of liver and spleen based on computational anatomy models. Comput. Biol. Med. 67, 146–160 (2015)

    Article  Google Scholar 

  11. LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time-series. In: Arbib, M.A. (ed.) The Handbook of Brain Theory and Neural Networks. MIT Press (1995)

    Google Scholar 

Download references


This research was supported in part by the Grant-in Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT) under the Grant No. 15H01130, 15K00253 and No. 16H01436, in part by the MEXT Support Program for the Strategic Research Foundation at Private Universities (2013-2017), and in part by the Recruitment Program of Global Experts HAIOU Program from Zhejiang Province, China.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Yen-Wei Chen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Todoroki, Y., Han, XH., Iwamoto, Y., Lin, L., Hu, H., Chen, YW. (2018). Detection of Liver Tumor Candidates from CT Images Using Deep Convolutional Neural Networks. In: Chen, YW., Tanaka, S., Howlett, R., Jain, L. (eds) Innovation in Medicine and Healthcare 2017. KES-InMed 2018 2017. Smart Innovation, Systems and Technologies, vol 71. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59396-8

  • Online ISBN: 978-3-319-59397-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics