Application of Deep Learning Algorithm in Cervical Cancer MRI Image Segmentation Based on Wireless Sensor

  • Peng Liang
  • Guijun Sun
  • Sirong WeiEmail author
Image & Signal Processing
Part of the following topical collections:
  1. Wearable Computing Techniques for Smart Health


With the development of medical technology in China, new difficulties are gradually emerging in traditional medicine. Cervical cancer MRI image segmentation technology based on wireless network is one of the most famous means. But the traditional technology is not strong enough for information processing and analysis. Manual data processing alone may lead to errors in data processing and so on. Therefore, this research was aimed at the MRI image segmentation technology of cervical cancer based on wireless network, using depth learning algorithm to calculate and analyze. Through this kind of wireless network and the computer algorithm form, the data processing ability can be improved and increase the data processing ability be increased.


Wireless network Cervical cancer MRI image segmentation technology Degree learning algorithm 


Compliance with ethical standards

All the authors of this article are aware of the content.

Conflict of interest

There is no conflict of interest in this article.

Human and animal studies

This article does not cover human participants and/or animal studies.


  1. 1.
    Cai, Y., Yu, F. R., Liang, C. et al., Software-defined device-to-device (D2D) communications in virtual wireless networks with imperfect network state information (NSI)[J]. IEEE Trans. Veh. Technol. 65(9):7349–7360, 2016.CrossRefGoogle Scholar
  2. 2.
    Gong, X., Duan, L., Chen, X. et al., When social network effect meets congestion effect in wireless networks: Data usage equilibrium and optimal pricing[J]. IEEE Journal on Selected Areas in Communications 35(2):449–462, 2017.CrossRefGoogle Scholar
  3. 3.
    El-Hihi, M., Attar, H., Solyman, A. A. A. et al., Network coding cooperation performance analysis in wireless network over a lossy channel, M users and a destination scenario[J]. Commun. Netw. 08(4):257–280, 2016.CrossRefGoogle Scholar
  4. 4.
    Gong, X., Trogh, J., Braet, Q. et al., Measurement-based wireless network planning, monitoring, and reconfiguration solution for robust radio communications in indoor factories[J]. IET Sci. Meas. Technol. 10(4):375–382, 2016.CrossRefGoogle Scholar
  5. 5.
    Zhu, L., Yu, F. R., Tang, T. et al., Handoff performance improvements in an integrated train-ground communication system based on wireless network virtualization[J]. IEEE Trans. Intell. Transp. Syst. PP(99):1–14, 2017.Google Scholar
  6. 6.
    Prahs, P., Radeck, V., Mayer, C. et al., OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications[J]. Graefes Arch. Clin. Exp. Ophthalmol.:1–8, 2017.Google Scholar
  7. 7.
    Brzezicki, M., and Kobetic, M., Using artificial intelligence deep learning algorithm to evaluate and guide the quality improvement work in neurosurgical practice[J]. Int. J. Surg. 47:S61, 2017.CrossRefGoogle Scholar
  8. 8.
    Anitha, J., Reddy, P. V. G. D. P., and Babu, M. S. P., Error tolerant global search incorporated with deep learning algorithm to automatic Hindi text summarization[J]. International Journal of Business Intelligence & Data Mining 1(1):1, 2017.CrossRefGoogle Scholar
  9. 9.
    Gulshan, V., Peng, L., Coram, M. et al., Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[J]. Jama 316(22):2402, 2016.CrossRefGoogle Scholar
  10. 10.
    Ibragimov, B., Pernuš, F., Strojan, P. et al., Development of a novel deep learning algorithm for autosegmentation of clinical tumor volume and organs at risk in head and neck radiation therapy planning[J]. Int. J. Radiat. Oncol. Biol. Phys. 96(2S):S226, 2016.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Yantaishan HospitalYantai CityPeople’s Republic of China
  2. 2.Imaging CenterGuangdong Sanjiu Brain HospitalGuangzhouChina
  3. 3.Department of Radiology107 Chinese People’s Liberation Army HospitalYantai CityChina

Personalised recommendations