Advertisement

Learning classification of big medical imaging data based on partial differential equation

  • Wenzhong ZhuEmail author
  • Lidong Xian
  • Erli Wang
  • Yani Hou
Original Research

Abstract

Traditional big medical image data classification methods are mostly based on the change of image gray features, extract edge and contour feature information, or perform conversion between medical image coordinate sets. However, the algorithms are complicated, real-time performance is poor, classification speed is slow, and accuracy is low. This paper proposes a classification study of big medical image data based on partial differential equations by combined with deep learning algorithms, and uses partial differential equations in big medical image processing to extract the texture features of medical images. Moreover, according to the texture features of the medical image contrast modulation, this paper filters out the image noise interference. Based on the depth learning algorithm, the image distance stratification, the target object size, the fit and other information, the accurate classification of big medical image data is realized. Experimental results show that the proposed classification method has high efficiency, low error rate, good real-time performance and robustness.

Keywords

Partial differential equations Big medical image data Medical images Texture features Contrast modulation 

Notes

Acknowledgements

This work is supported by the following programs. (1) Sichuan Science and technology projects (18ZDYF2517); (2) Zigong science and Technology Bureau (2016DZ11); (3) Sichuan Provincial Academician (Expert) Workstation (2015YSGZZ04 and 2016YSGZZ02); Key Laboratory Higher Education of Sichuan Province for Enterprise Informationalization and IOT (2015WZJ01); Sichuan Provincial Key research Base of Intelligent Tourism (ZHY17-02).

References

  1. Ahmad I, Siraj-ul-Islam, Khaliq AQM (2017) Local RBF method for multi-dimensional partial differential equations. Comput Math Appl 74(2):292–324.  https://doi.org/10.1016/j.camwa.2017.04.026 MathSciNetzbMATHGoogle Scholar
  2. Ali R, Pal AK, Kumari S, Sangaiah AK, Li X, Wu FJ (2018) An enhanced three factor based authentication protocol using wireless medical sensor networks for healthcare monitoring. J Ambient Intell Hum Comput.  https://doi.org/10.1007/s12652-018-1015-9 (first online) Google Scholar
  3. Aziz I, Siraj-ul-Islam, Asif M (2017) Haar wavelet collocation method for three-dimensional elliptic partial differential equations. Comput Math Appl 73(9):2023–2034.  https://doi.org/10.1016/j.camwa.2017.02.034 MathSciNetzbMATHGoogle Scholar
  4. Boiger R, Kaltenbacher B (2016) An online parameter identification method for time dependent partial differential equations. Inverse Probl 32:045006.  https://doi.org/10.1088/0266-5611/32/4/045006 MathSciNetzbMATHGoogle Scholar
  5. Fang C, Zhao Z, Zhou P, Lin Z (2017) Feature learning via partial differential equation with applications to face recognition. Pattern Recognit 69:14–25.  https://doi.org/10.1016/j.patcog.2017.03.034 Google Scholar
  6. Galán-García JL, Aguilera-Venegas G (2017) Partial differential equations based simulations in multiple space dimensions. Comput Math Appl 74(1):1–5.  https://doi.org/10.1016/j.camwa.2017.05.001 MathSciNetzbMATHGoogle Scholar
  7. Gaocheng L, Shuai L, Muhammad K, Sangaiah AK, Doctor F (2018) Object tracking in vary lighting conditions for fog based intelligent surveillance of public spaces. IEEE Access 6:29283–29296.  https://doi.org/10.1109/ACCESS.2018.2834916 Google Scholar
  8. Hattaf K, Yousfi N (2016) A numerical method for delayed partial differential equations describing infectious diseases. Comput Math Appl 72(11):2741–2750.  https://doi.org/10.1016/j.camwa.2016.09.024 MathSciNetzbMATHGoogle Scholar
  9. King ND, Ruuth SJ (2017) Solving variational problems and partial differential equations that map between manifolds via the closest point method. J Comput Phys 336:330–346.  https://doi.org/10.1016/j.jcp.2017.02.019 MathSciNetzbMATHGoogle Scholar
  10. Lee K, Elman HC (2017) A preconditioned low-rank projection method with a rank-reduction scheme for stochastic partial differential equations. SIAM J Sci Comput 39(5):S828–S850.  https://doi.org/10.1137/16M1075582 MathSciNetzbMATHGoogle Scholar
  11. Li S, Yang X (2017) Novel image inpainting algorithm based on adaptive fourth-order partial differential equation. IET Image Process 11(10):870–879.  https://doi.org/10.1049/iet-ipr.2016.0898 Google Scholar
  12. Liu S, Pan Z, Fu W, Cheng X (2017) Fractal generation method based on asymptote family of generalized Mandelbrot set and its application. J Nonlinear Sci Appl 10(3):1148–1161.  https://doi.org/10.22436/jnsa.010.03.24 MathSciNetGoogle Scholar
  13. Malathi D, Logesh R, Subramaniyaswamy V, Vijayakumar V, Sangaiah AK (2019) Hybrid reasoning-based privacy-aware disease prediction support system. Comput Electr Eng 73:114–127.  https://doi.org/10.1016/j.compeleceng.2018.11.009 Google Scholar
  14. Nnolim UA (2017) Improved partial differential equation-based enhancement for underwater images using local–global contrast operators and fuzzy homomorphic processes. IET Image Process 11(11):1059–1067.  https://doi.org/10.1049/iet-ipr.2017.0259 Google Scholar
  15. Phapatanaburi K, Wang L, Oo Z, Li W, Nakagawa S, Iwahashi M (2017) Noise robust voice activity detection using joint phase and magnitude based feature enhancement. J Ambient Intell Hum Comput 8(6):845–859.  https://doi.org/10.1007/s12652-017-0482-8 Google Scholar
  16. Pu YF, Siarry P, Chatterjee A, Wang ZN, Yi Z, Liu YG, Zhou JL, Wang Y (2018) A fractional-order variational framework for Retinex: fractional-order partial differential equation-based formulation for multi-scale nonlocal contrast enhancement with texture preserving. IEEE Trans Image Process 27(3):1214–1229.  https://doi.org/10.1109/TIP.2017.2779601 MathSciNetGoogle Scholar
  17. Raissi M, Karniadakis GE (2018) Hidden physics models: machine learning of nonlinear partial differential equations. J Comput Phys 357:125–141.  https://doi.org/10.1016/j.jcp.2017.11.039 MathSciNetzbMATHGoogle Scholar
  18. Sharma D, Bhondekar AP, Shukla AK, Ghanshyam C (2016) A review on technological advancements in crowd management. J Ambient Intell Hum Comput 9(3):485–495.  https://doi.org/10.1007/s12652-016-0432-x Google Scholar
  19. Shuai L, Weina F, Liqiang H, Jiantao Z, Ming M (2017) Distribution of primary additional errors in fractal encoding method. Multimed Tools Appl 76(4):5787–5802.  https://doi.org/10.1007/s11042-014-2408-1 Google Scholar
  20. Shuai L, Weiling B, Gaocheng L, Wenhui L, Hari S (2018) Parallel fractal compression method for big video data. Complexity 2018:2016976.  https://doi.org/10.1155/2018/2016976 Google Scholar
  21. Yang D (2017) Non-iterative parallel Schwarz algorithms based on overlapping domain decomposition for parabolic partial differential equations. Math Comput 86:2687–2718.  https://doi.org/10.1090/mcom/3102 MathSciNetzbMATHGoogle Scholar
  22. Yang TH, Wu CH, Huang KY, Su MH (2017) Coupled HMM-based multimodal fusion for mood disorder detection through elicited audio–visual signals. J Ambient Intell Hum Comput 8(6):895–906.  https://doi.org/10.1007/s12652-016-0395-y Google Scholar
  23. Zhang C, Li D, Broumi S, Sangaiah AK (2018) Medical diagnosis based on single-valued neutrosophic probabilistic rough multisets over two universes. Symmetry-Basel 10(6):213.  https://doi.org/10.3390/sym10060213 Google Scholar
  24. Zheng HT, Han J, Chen J, Sangaiah AK (2018a) A novel framework for Automatic Chinese Question Generation based on multi-feature neural network model. Comput Sci Inf Syst 15(3):487–499.  https://doi.org/10.2298/CSIS171121018Z Google Scholar
  25. Zheng P, Shuai L, Sangaiah AK, Muhammad K (2018b) Visual attention feature (VAF): a novel strategy for visual tracking based on cloud platform in intelligent surveillance systems. J Parallel Distrib Comput 120:182–194.  https://doi.org/10.1016/j.jpdc.2018.06.012 Google Scholar
  26. Zheng Z, Sangaiah AK, Wang T (2018c) Adaptive communication protocols in flying ad hoc network. IEEE Commun Mag 56(1):136–142.  https://doi.org/10.1109/MCOM.2017.1700323 Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Wenzhong Zhu
    • 1
    • 3
    Email author
  • Lidong Xian
    • 2
  • Erli Wang
    • 1
  • Yani Hou
    • 1
  1. 1.School of Computer ScienceSichuan University of Science and EngineeringZigongChina
  2. 2.School of Film and TelevisionSichuan Vocational College of Cultural IndustriesChengduChina
  3. 3.Chengdu Jinyang Hi-tech Development Co., Ltd.ChengduChina

Personalised recommendations