ArticleHealth information science and systems2018
Ensemble of subspace discriminant classifiers for schistosomal liver fibrosis staging in mice microscopic images.
Article in Health information science and systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 53 citations in OpenAlex.
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Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.