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Investigating brain tumor classification using MRI: a scientometric analysis of selected articles from 2015 to 2024.

Background: Magnetic resonance imaging (MRI) is a non-invasive method widely used to evaluate abnormal tissues, especially in the brain. While many studies have examined brain tumor classification using MRI, a comprehensive scientometric analysis remains limited. Objective: This study aimed to inves...

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Published in:Neuroradiology Vol. 67; no. 10; pp. 2635 - 2673
Main Authors: Mounika, Gunde, Kollem, Sreedhar, Samala, Srinivas
Format: diagnostic images equations & formulas pictorial review tables/charts Journal Article
Published: Springer Nature Oct2025
Online Access:View this record in EBSCOhost
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      dt: Oct2025
      vid: 67
      iid: 10
      pid: 237
      pub: Springer Nature
      place: New York, New York
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        atl: Investigating brain tumor classification using MRI: a scientometric analysis of selected articles from 2015 to 2024.
      aug:
        au:
          Mounika, Gunde
          Kollem, Sreedhar
          Samala, Srinivas
        affil: https://ror.org/017ebfz38 Department of ECE, SR University, 506371, Warangal, Telangana, India
      sug:
        subj:
          Brain Neoplasms Classification
          Magnetic Resonance Imaging Utilization
          Publishing Trends
          Citation Analysis
          Convolutional Neural Networks
          Workflow
          Authorship
          Maps
          Serial Publications
          Bibliometrics
          International Agencies
          Collaboration
          Deep Learning
          Thematic Analysis
      ab: Background: Magnetic resonance imaging (MRI) is a non-invasive method widely used to evaluate abnormal tissues, especially in the brain. While many studies have examined brain tumor classification using MRI, a comprehensive scientometric analysis remains limited. Objective: This study aimed to investigate brain tumor classification based on magnetic resonance imaging (MRI), using scientometric approaches, from 2015 to 2024. Methods: A total of 348 peer-reviewed articles were extracted from the Scopus database. Tools such as CiteSpace and VOSviewer were employed to analyze key metrics, including citation frequency, author collaboration, and publication trends. Results: The analysis revealed top authors, top-cited journals, and international collaborations. Co-occurrence networks identified the top research topics and bibliometric coupling revealed knowledge advancements in the domain. Conclusion: Deep learning methods are increasingly used in brain tumor classification research. This study outlines the current trends, uncovers research gaps, and suggests future directions for researchers in the domain of MRI-based brain tumor classification.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        pictorial
        review
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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