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Schistosomal Hepatic Fibrosis Classification

Author

Listed:
  • Dalia S. Ashour

    (Department of Medical Parasitology, Faculty of Medicine, Tanta University, Tanta, Egypt)

  • Dina M. Abou Rayia

    (Department of Medical Parasitology, Faculty of Medicine, Tanta University, Tanta, Egypt)

  • Nilanjan Dey

    (Techno India College of Technology, West Bengal, India)

  • Amira S. Ashour

    (Department of Electronics and Electrical Communication Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt)

  • Ahmed Refaat Hawas

    (Department of Electronics and Electrical Communication Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt)

  • Manar B. Alotaibi

    (Computers and Information Technology, Taif University, Ta'if, Saudi Arabia)

Abstract

Schistosomiasis is serious liver tissues' parasitic disease that leads to liver fibrosis. Microscopic liver tissue images at different stages can be used for assessment of the fibrosis level. In the current article, the different stages of granuloma were classified after features extraction. Statistical features extraction was used to extract the significant features that characterized each stage. Afterward, different classifiers, namely the Decision Tree, Nearest Neighbor and the Neural Network are employed to carry out the classification process. The results established that the cubic k-NN, cosine k-NN and medium k-NN classifiers achieved superior classification accuracy compared to the other classifiers with 88.3% accuracy value.

Suggested Citation

  • Dalia S. Ashour & Dina M. Abou Rayia & Nilanjan Dey & Amira S. Ashour & Ahmed Refaat Hawas & Manar B. Alotaibi, 2018. "Schistosomal Hepatic Fibrosis Classification," International Journal of Natural Computing Research (IJNCR), IGI Global, vol. 7(2), pages 1-17, April.
    • Handle: RePEc:igg:jncr00:v:7:y:2018:i:2:p:1-17
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