Synthesis of a neural network classifier for hepatocellular carcinoma grading based on triphasic CT images

Abstract

Computer Aided Decision (CAD) systems based on Medical Imaging could support radiologists in grading Hepatocellular carcinoma (HCC) by means of Computed Tomography (CT) images, avoiding that patient undergo any medical invasive procedures such as biopsies. The individuation and characterization of Regions of Interest (ROIs) containing lesions is an important phase that enables an easier classification between two classes of HCCs. Two phases are needed for the individuation of lesioned ROIs: a liver isolation in each CT slice, and a lesion segmentation. Ultimately, all individuated ROIs are described by morphological features and, finally, a feed-forward supervised Artificial Neural Network (ANN) is used to classify them. Testing determined that the ANN topologies found through an evolutionary strategy showed a high generalization on the mean performance indices regardless of applied training, validation and test sets, showing good performances in terms of both accuracy and sensitivity, permitting a correct grading of HCC lesions.