Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy ass...
Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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We propose a random forest classifier for identifying adequacy of liver MR images using handcrafted (HC) features and deep convolutional neural networks (CNNs), and analyze the relative role of these two components in relation to the training sample size. The HC features, specifically developed for this application, include Gaussian mixture models,...
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Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment
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TN_cdi_doaj_primary_oai_doaj_org_article_8163ba569a264ccba16c94fb8f0d2882
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_8163ba569a264ccba16c94fb8f0d2882
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-020-77264-y
