Experimenting various classification techniques for improving the automatic diagnosis of the malignant liver tumors, 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 develop 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 hepatocellular carcinoma is also divided into subclasses, and the multiclass classification techniques are experimented for accuracy improvement.