Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.21/3010
Título: Classification and staging of chronic liver disease from multimodal data
Autor: Ribeiro, Ricardo
Marinho, Rui
Sanches, João
Palavras-chave: Chronic liver disease
Ultrasound-based textural features
Feature extraction
Medical diagnostic imaging
Support vector machines
Data: Mai-2013
Editora: IEEE
Citação: Ribeiro R, Marinho R, Sanches J. Classification and staging of chronic liver disease from multimodal data. IEEE Trans Biomed Eng. 2013;60(5):1336-44.
Resumo: Chronic liver disease (CLD) is most of the time an asymptomatic, progressive, and ultimately potentially fatal disease. In this study, an automatic hierarchical procedure to stage CLD using ultrasound images, laboratory tests, and clinical records are described. The first stage of the proposed method, called clinical based classifier (CBC), discriminates healthy from pathologic conditions. When nonhealthy conditions are detected, the method refines the results in three exclusive pathologies in a hierarchical basis: 1) chronic hepatitis; 2) compensated cirrhosis; and 3) decompensated cirrhosis. The features used as well as the classifiers (Bayes, Parzen, support vector machine, and k-nearest neighbor) are optimally selected for each stage. A large multimodal feature database was specifically built for this study containing 30 chronic hepatitis cases, 34 compensated cirrhosis cases, and 36 decompensated cirrhosis cases, all validated after histopathologic analysis by liver biopsy. The CBC classification scheme outperformed the nonhierachical one against all scheme, achieving an overall accuracy of 98.67% for the normal detector, 87.45% for the chronic hepatitis detector, and 95.71% for the cirrhosis detector.
Peer review: yes
URI: http://hdl.handle.net/10400.21/3010
ISSN: 1558-2531
Versão do Editor: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6387584
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