Chronic liver disease staging classification based on ultrasound, clinical and ...

• Author(s): Ribeiro, Ricardo ; Marinho, Rui ; Velosa, José ; Ramalho, Fernando ; Sanches, João
• Date: 2011
• Persistent ID: http://hdl.handle.net/10400.21/3014
• Origin: Repositório Científico do Instituto Politécnico de Lisboa
• Subject(s): Chronic liver disease; Classification; Tissue characterization; Ultrasound; Biomedical imaging; Feature extraction; Support vector machines; Ultrasonic imaging
• Document Type: Article
• Language: English

Description

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.