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Texture feature ranking with relevance learning to classify interstitial lung disease patterns
OBJECTIVE: The generalized matrix learning vector quantization (GMLVQ) is used to estimate the relevance of texture features in their ability to classify interstitial lung disease patterns in high-resolution computed tomography images. METHODOLOGY: After a stochastic gradient descent, the GMLVQ algo...
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| Main Authors: | , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2012
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4096044/ https://ncbi.nlm.nih.gov/pubmed/23010586 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.artmed.2012.07.001 |
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