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Medical & Biological Engineering & Computing

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16-10-2017 | Original Article

Novel Mahalanobis-based feature selection improves one-class classification of early hepatocellular carcinoma

Authors: Ricardo de Lima Thomaz, Pedro Cunha Carneiro, João Eliton Bonin, Túlio Augusto Alves Macedo, Ana Claudia Patrocinio, Alcimar Barbosa Soares

Published in: Medical & Biological Engineering & Computing | Issue 5/2018

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Abstract

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.

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Title: Novel Mahalanobis-based feature selection improves one-class classification of early hepatocellular carcinoma
Authors: Ricardo de Lima Thomaz
Pedro Cunha Carneiro
João Eliton Bonin
Túlio Augusto Alves Macedo
Ana Claudia Patrocinio
Alcimar Barbosa Soares
Publication date: 16-10-2017
Publisher: Springer Berlin Heidelberg
Published in: Medical & Biological Engineering & Computing / Issue 5/2018
Print ISSN: 0140-0118
Electronic ISSN: 1741-0444
DOI: https://doi.org/10.1007/s11517-017-1736-5

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