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Schistosomal Hepatic Fibrosis Classification

Schistosomal Hepatic Fibrosis Classification

Dalia S. Ashour, Dina M. Abou Rayia, Nilanjan Dey, Amira S. Ashour, Ahmed Refaat Hawas, Manar B. Alotaibi
Copyright: © 2018 |Volume: 7 |Issue: 2 |Pages: 17
ISSN: 1947-928X|EISSN: 1947-9298|EISBN13: 9781522544814|DOI: 10.4018/IJNCR.2018040101
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MLA

Ashour, Dalia S., et al. "Schistosomal Hepatic Fibrosis Classification." IJNCR vol.7, no.2 2018: pp.1-17. http://doi.org/10.4018/IJNCR.2018040101

APA

Ashour, D. S., Abou Rayia, D. M., Dey, N., Ashour, A. S., Hawas, A. R., & Alotaibi, M. B. (2018). Schistosomal Hepatic Fibrosis Classification. International Journal of Natural Computing Research (IJNCR), 7(2), 1-17. http://doi.org/10.4018/IJNCR.2018040101

Chicago

Ashour, Dalia S., et al. "Schistosomal Hepatic Fibrosis Classification," International Journal of Natural Computing Research (IJNCR) 7, no.2: 1-17. http://doi.org/10.4018/IJNCR.2018040101

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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.

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