Liver Disease Detection and Classification - A Bibliometric Survey
Abstract
In this paper, a bibliometric survey has been carried out on liver disease detection and classification from 1965 to 2021. The liver is one of the most important organs in the human body. Recent trends in research are based on artificial intelligence and machine learning. More specifically, neural networks are used to detect abnormal regions and to classify the images. The Scopus database has been used to analyses the documents published on this topic. There were a total of 1460 documents found on the topic of liver disease detection and classification. The statistical analysis is carried out source-wise, on methods used to detect liver diseases, specifically image processing methods, AI, NN, and advanced methodologies, year-wise, area-wise, country-wise, university-wise, and based on funding agency. Network analysis is also carried out based on co-authorship, co-occurrence, Citation Analysis, and Bibliographic coupling. The analysis shows that the number of papers published is increasing year-wise from 2000 to 2020. This shows that there is still a great scope for research on this topic. The highest number of publications was in the year 2019. The number of documents published this year is 130. VOSviewer 1.6.16 software is used for statistical analysis and network analysis of the database. It provides a very effective way to analyze the co-authorship, co-occurrences, citations and bibliometric couplings etc. The source for all tables and figures is www.scopus.com.The data was assessed on July 17, 2021.