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Ensemble of subspace discriminant classifiers for schistosomal liver fibrosis staging in mice microscopic images

  • Amira S. Ashour
  • Yanhui Guo
  • Ahmed Refaat Hawas
  • Guan Xu
Research
  • 23 Downloads
Part of the following topical collections:
  1. Special Issue on Application of Artificial Intelligence in Health Research

Abstract

Schistosomiasis is one of the dangerous parasitic diseases that affect the liver tissues leading to liver fibrosis. Such disease has several levels, which indicate the degree of fibrosis severity. To assess the fibrosis level for diagnosis and treatment, the microscopic images of the liver tissues were examined at their different stages. In the present work, an automated staging method is proposed to classify the statistical extracted features from each fibrosis stage using an ensemble classifier, namely the subspace ensemble using linear discriminant learning scheme. The performance of the subspace/discriminant ensemble classifier was compared to other ensemble combinations, namely the boosted/trees ensemble, bagged/trees ensemble, subspace/KNN ensemble, and the RUSBoosted/trees ensemble. The simulation results established the superiority of the proposed subspace/discriminant ensemble with 90% accuracy compared to the other ensemble classifiers.

Keywords

Liver fibrosis Schistosomiasis Ensemble classifier Statistical features 

Notes

Acknowledgements

The authors are thankful to Dr. Dalia Salah Ashour and Dina M. Abou Rayia, Department of Medical Parasitology, Faculty of Medicine, Tanta University, Egypt, for performing the parasitology part of the study and providing us with the used microscopic images dataset at the different fibrosis stages as well as the normal case.

References

  1. 1.
    Chaves NJ, Gibney KB, Leder K, O’brien DP, Marshall C, Biggs BA. Screening practices for infectious diseases among Burmese refugees in Australia. Emerging Infectious Dis. 2009;15(11):1769.CrossRefGoogle Scholar
  2. 2.
    Xia JL, Dai C, Michalopoulos GK, Liu Y. Hepatocyte growth factor attenuates liver fibrosis induced by bile duct ligation. The American J Pathol. 2006;168(5):1500–12.CrossRefGoogle Scholar
  3. 3.
    Sun W, Chang S, Tai DC, Tan N, Xiao G, Tang H, Yu H. Nonlinear optical microscopy: use of second harmonic generation and two-photon microscopy for automated quantitative liver fibrosis studies. J Biomed Opt. 2008;13(6):064010.CrossRefGoogle Scholar
  4. 4.
    Mabey D, Peeling RW, Ustianowski A, Perkins MD. Tropical infectious diseases: diagnostics for the developing world. Nat Rev Microbiol. 2004;2(3):231.CrossRefGoogle Scholar
  5. 5.
    Mahmoud-Ghoneim D. Optimizing automated characterization of liver fibrosis histological images by investigating color spaces at different resolutions. Theor Biol Med Modell. 2011;8(1):25.CrossRefGoogle Scholar
  6. 6.
    Ali S, Smith KA. On learning algorithm selection for classification. Appl Soft Comput. 2006;6(2):119–38.CrossRefGoogle Scholar
  7. 7.
    Kuncheva LI. Combining pattern classifiers: methods and algorithms. New York: Wiley; 2004.CrossRefGoogle Scholar
  8. 8.
    Woods K, Kegelmeyer WP, Bowyer K. Combination of multiple classifiers using local accuracy estimates. IEEE Trans Pattern Anal Mach Intell. 1997;19(4):405–10.CrossRefGoogle Scholar
  9. 9.
    Polikar R. Ensemble based systems in decision making. IEEE Circuits Syst Mag. 2006;6(3):21–45.CrossRefGoogle Scholar
  10. 10.
    Polikar R. Ensemble based systems in decision making. IEEE Circuits Syst Mag. 2006;6(3):21–45.CrossRefGoogle Scholar
  11. 11.
    Zhang C, Ma Y, editors. Ensemble machine learning: methods and applications. New York: Springer Science & Business Media; 2012.zbMATHGoogle Scholar
  12. 12.
    Rahman A, Verma B. Cluster-based ensemble of classifiers. Exp Syst. 2013;30(3):270–82.CrossRefGoogle Scholar
  13. 13.
    Tao D, Tang X, Li X, Wu X. Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans Pattern Anal Mach Intell. 2006;28(7):1088–99.CrossRefGoogle Scholar
  14. 14.
    García-Pedrajas N, Ortiz-Boyer D. Boosting random subspace method. Neural Netw. 2008;21(9):1344–62.CrossRefGoogle Scholar
  15. 15.
    Kotsiantis S. Combining bagging, boosting, rotation forest and random subspace methods. Artif Intell Rev. 2011;35(3):223–40.CrossRefGoogle Scholar
  16. 16.
    Ho TK. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell. 1998;20(8):832–44.CrossRefGoogle Scholar
  17. 17.
    Kuncheva LI, Rodríguez JJ, Plumpton CO, Linden DE, Johnston SJ. Random subspace ensembles for fMRI classification. IEEE Trans Med Imaging. 2010;29(2):531–42.CrossRefGoogle Scholar
  18. 18.
    Panov P, Džeroski S. Combining bagging and random subspaces to create better ensembles. In: International Symposium on Intelligent Data Analysis. Springer, Berlin, Heidelberg; 2007. pp. 118-129.Google Scholar
  19. 19.
    Skurichina M, Duin RP. Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal Appl. 2002;5(2):121–35.MathSciNetCrossRefGoogle Scholar
  20. 20.
    Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M,… & Sánchez CI. A survey on deep learning in medical image analysis. Medical Image Anal. 2017;42:60–88.CrossRefGoogle Scholar
  21. 21.
    Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221–48.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Electronics and Electrical Communication Engineering, Faculty of EngineeringTanta UniversityTantaEgypt
  2. 2.Department of Computer ScienceUniversity of Illinois at SpringfieldSpringfieldUSA
  3. 3.Department of RadiologyUniversity of Michigan Medical SchoolAnn ArborUSA

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