Analysis on segmentation and biomarker‐based approaches for liver cancer detection: A survey

Abstract

Liver cancer is the major reason for death in this entire world. Manual detection of cancer tissue is found time consuming and difficult. Therefore, the development of an automatic detection approach with high accuracy for liver cancer is considered as the main aim of this work. The image processing approach can use the CAD for the classification of liver cancer in order to assist the physician in the decision-making process. An automated approach to effective classification of the liver tumour using effective features is conveyed in terms of the CAD system. Traditionally, radiologists delineate the liver and liver lesion on a slice-by-slice basis, which is time consuming and susceptible to inter- and intra-rater variations. Automatic methods for the segmentation of liver and liver tumours are, thus, highly demanded in clinical practice. A systematic review is being carried out to detect reported liver cancer from 2013 to 2020. Finally, a more precise technical direction is provided for all researchers in this review. Research gaps for earlier detection and its potential future aspects are also discussed.