A Critical Review on Machine Learning based Liver Tumor Classification

Abstract

Diagnosis of cancer and its treatment is of widespread significance because of the regular incidence of cancers and the frequency after treatment. The liver is the second organ most typically included by metastatic sickness, being liver disease the noticeable reason for death around the world. The early location of tumors is basic for the treatment of liver tumors. There are usually three different approaches to recognize liver cancer, such as blood tests, image tests, and biopsy. Computed tomography is a regularly used method for liver malignancy checking and treatment purposes. Automated liver tumor segmentation of CT images is a demanding problem. Image processing is applied to identify liver tumors. Image processing is a method of re-performing a few operations on an image. Steps for liver tumor segmentation using image processing includes image acquisition, preprocessing, liver segmentation, tumor segmentation, and classification. This article discusses the types, signs, symptoms, various tests for detecting tumors, stages of liver malignancy, and various image processing methods for tumor segmentation in the literature.