Chapter 6 - Comparison of multiclass and hierarchical CAC design for benign and malignant hepatic tumors

Abstract

In the current study, an efficient smooth support vector machine (SSVM)-based hierarchical CAC system has been designed for primary benign and malignant focal hepatic tumors. The work has been carried out on a representative and robust image dataset consisting of 76 liver ultrasound images with (a) 16 hemangioma (typical and atypical HEM) images, (b) 28 hepatocellular carcinoma (small HCC and large HCC) images, and (c) 32 metastatic carcinoma (typical and atypical MET) images. The textural characteristics from inside the regions and outside the FHTs are considered equally important for the differential diagnosis. Therefore, in the current study, the inside regions of interest (IROIs) have been selected from within the FHTs and surrounding regions of interest (SROIs) have been selected from the homogeneous region around the lesion and at the same depth as that of the lesion center. Five texture features are computed from the collective dataset consisting of 255 IROIs. In the current study, the SSVM-based multiclass CAC system design has been compared with the SSVM-based hierarchical CAC system design. The SSVM-based hierarchical CAC system consists of two binary classifiers that are arranged in a hierarchical framework. The SSVM classifier-1 classifies the HEM, HCC, and MET images into primary benign (HEM) and malignant (HCC or MET) cases. The malignant cases are further classified by the SSVM classifier-2 into primary malignant (HCC) and secondary malignant (MET) cases. The overall classification accuracy achieved for the multiclass classifier is 82.6% with 20 misclassification cases out of 115 test instances. However, it has been observed that the hierarchical CAC system yields n overall classification accuracy of 89.6% with 12 misclassification cases out of 115 test instances.