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 |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129156220&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129156220 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2018 vid: 56 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 129156220 129156220 NLM29034407 10.1007/s11517-017-1736-5 NLM29034407 129156220 ppf: 817 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Novel Mahalanobis-based feature selection improves one-class classification of early hepatocellular carcinoma. aug: au: Thomaz, Ricardo de Lima Carneiro, Pedro Cunha Bonin, João Eliton Macedo, Túlio Augusto Alves Patrocinio, Ana Claudia Soares, Alcimar Barbosa affil: Biomedical Engineering Lab, Faculty of Electrical Engineering, Federal University of Uberlândia, Av. João Naves de Ávila 2121, 38408-100, Uberlândia, MG, Brazil sug: subj: Liver Neoplasms Classification Algorithms Carcinoma, Hepatocellular Classification Carcinoma, Hepatocellular Reproducibility of Results ROC Curve Liver Neoplasms Multidetector Computed Tomography ab: 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 the target class. Although the literature outlines several features for characterizing liver lesions, it is unclear which is most relevant for describing early HCC. In this paper, we introduce an unconstrained GA feature selection algorithm based on a multi-objective Mahalanobis fitness function to improve the classification performance for early HCC. We compared our approach to a constrained Mahalanobis function and two other unconstrained functions using Welch's t-test and Gaussian Data Descriptors. The performance of each fitness function was evaluated by cross-validating a one-class SVM. The results show that the proposed multi-objective Mahalanobis fitness function is capable of significantly reducing data dimensionality (96.4%) and improving one-class classification of early HCC (0.84 AUC). Furthermore, the results provide strong evidence that intensity features extracted at the arterial to portal and arterial to equilibrium phases are important for classifying early HCC. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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