Advanced classification methods for improving the automatic diagnosis of the hepatocellular carcinoma, based on ultrasound images

The hepatocellular carcinoma (HCC) is the most frequent malignant liver tumor. Nowadays, the only reliable method for the detection of HCC is the needle biopsy, but it is invasive, dangerous for the patient. We aim to elaborate a non-invasive method for the automatic diagnosis of HCC, based only on computerized techniques for ultrasound image analysis. Thus, we elaborated the imagistic textural model of HCC, consisting in the exhaustive set of the textural parameters, relevant for HCC characterization, and in their specific values for the HCC class. In this work, we study the effect of the classifier combination procedures on the improvement of the recognition performance, from speed and accuracy points of view. Various combination schemes are considered, and their influence on the accuracy parameters and on the learning curves is discussed. The role of the dimensionality reduction methods in the improvement of the automatic diagnosis process is discussed as well.