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

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


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.


Liver fibrosis Schistosomiasis Ensemble classifier Statistical features 



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.


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© 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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