Multilevel local region sparse shape composition model for liver cancer classification

Abstract

Machine learning and computer-assisted disease detection are two technological innovations that have significantly improved medical advancement, and recent research has demonstrated their effectiveness. Liver cancer is one of the important causes of cancer-related deaths internationally. Tumor detection in the liver and its classification is challenging due to poor accuracy and high processing requirements available in the existing techniques. In these, there is a loss of edge information and the quality of the images considered. To address these issues in the segmentation and classification of liver cancer, a novel feature extraction method and algorithm called the novel multilevel local region (MLR)-based sparse shape composition model (NMLR-SSC) were developed. This innovative technique identifies the existence of tumors on abdominal computed tomography (CT) images. The input images were obtained from the 3D-IRCADb-01 dataset. The algorithm showed an improvement of 0.22%. When compared to other classifiers. The proposed algorithm showed 100% specificity, sensitivity, and a 98% accuracy rate in detecting the liver tumor.