Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

• Autor(a) principal: Ribeiro, Ricardo
• Data de Publicação: 2011
• Outros Autores: Marinho, Rui, Velosa, José, Ramalho, Fernando, Sanches, João
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.21/3014
• topic: Chronic liver disease; Classification; Tissue characterization; Ultrasound; Biomedical imaging; Feature extraction; Support vector machines; Ultrasonic imaging

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging.