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A systematic review of deep learning-based segmentation techniques for brain tumor detection (2013-2023)
Farrukh Hassan1, Saad Aslam1, Samuel-Soma M Ajibade1
1School of Engineering and Technology, Sunway University, Bandar Sunway, Selangor Darul Ehsan, Malaysia.
Objective:
To investigate the publication trends and research landscape of deep learning-based image segmentation techniques for brain tumor detection, focusing on the period from 2013 to 2023, and to identify key stakeholders, influential research, and prevalent research themes.
Methods:
This systematic review utilized the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines and bibliometric analysis methods. Databases including Scopus, PubMed, and Web of Science were searched for publications from 2013 to 2023 using a defined search query. Analysis included publication trends, stakeholder identification (authors, institutions, funding bodies, countries), top-cited literature, co-authorship, keyword co-occurrence, and citation analysis using VOSviewer software and the results of the PubMed were analyzed by R Studio.
Results:
A total of 931 documents were analyzed after PRISMA filtering. The number of publications increased substantially from 1 to 310 during the review period. Journal articles were the predominant document type. Tongxue Zhou was the most prolific researcher, and the Ministry of Education of the People's Republic of China, Imperial College of London, and Harvard Medical School were the most active affiliations. The National Natural Science Foundation of China was the leading funding organization. Keyword co-occurrence analysis highlighted the prevalence of "deep learning brain tumor image segmentation." Co-authorship and citation analyses revealed key collaborations and influential publications.
Conclusion:
This study provides valuable insights into the research landscape of deep learning for brain tumor image segmentation. The identified trends, stakeholders, and research themes can inform future research directions and policy decisions in this rapidly evolving field. The findings highlight the growth and multidisciplinary nature of this research area, while also suggesting potential opportunities and challenges for future development in brain tumor image segmentation techniques.