Novel Mahalanobis-based feature selection improves one-class classification of early hepatocellular carcinoma.
Detection of early hepatocellular carcinoma (HCC) is responsible for increasing survival rates in up to 40%. One-class classifiers can be used for modeling early HCC in multidetector computed tomography (MDCT), but demand the specific knowledge pertaining to the set of features that best describes t...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 5; pp. 817 - 833 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2018
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| Acceso en línea: | Ver este registro en EBSCOhost |