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Advisor(s)
Abstract(s)
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.
Description
Keywords
Chronic liver disease Classification Tissue characterization Ultrasound Biomedical imaging Feature extraction Support vector machines Ultrasonic imaging
Citation
Ribeiro R, Marinho R, Velosa J, Ramalho F, Sanches J. Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data. In From Nano to Macro, 2011 IEEE International Symposium on Biomedical Imaging. IEEE; 2011. p. 707-10.