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Advisor(s)
Abstract(s)
Chronic Liver Disease is a progressive, most of the time asymptomatic, and potentially fatal disease. In this paper, a semi-automatic procedure to stage this disease is proposed based on ultrasound liver images, clinical and laboratorial data. In the core of the algorithm two classifiers are used: a k nearest neighbor and a Support Vector Machine, with different kernels. The classifiers were trained with the proposed multi-modal feature set and the results obtained were compared with the laboratorial and clinical feature set. The results showed that using ultrasound based features, in association with laboratorial and clinical features, improve the classification accuracy. The support vector machine, polynomial kernel, outperformed the others classifiers in every class studied. For the Normal class we achieved 100% accuracy, for the chronic hepatitis with cirrhosis 73.08%, for compensated cirrhosis 59.26% and for decompensated cirrhosis 91.67%.
Description
Keywords
Kernel Laboratories Liver Polynomials Sensitivity Support vector machines Ultrasonic imaging Algorithms Artificial intelligence End stage liver disease Image enhancement Sensitivity and specificity Ultrasonography Image interpretation, Computer-assisted
Citation
Ribeiro R, Marinho R, Velosa J, Ramalho F, Sanches J, Suri JS. The usefulness of ultrasound in the classification of chronic liver disease. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE; 2011. p. 5132-5.