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Investigating brain tumor classification using MRI: a scientometric analysis of selected articles from 2015 to 2024
Gunde Mounika1, Sreedhar Kollem2, Srinivas Samala1
1Department of ECE, SR University, Warangal, Telangana, India, 506371.
Neuroradiology
|July 18, 2025
Summary
This study analyzed brain tumor classification using magnetic resonance imaging (MRI) research from 2015-2024. Deep learning is a growing trend, with scientometric analysis revealing key topics and future research directions.
Area of Science:
- Medical Imaging
- Radiology
- Bibliometrics
Background:
- Magnetic Resonance Imaging (MRI) is crucial for non-invasive evaluation of brain abnormalities.
- Existing research on brain tumor classification using MRI lacks comprehensive scientometric analysis.
Purpose of the Study:
- To conduct a scientometric analysis of brain tumor classification research using MRI.
- To investigate trends from 2015 to 2024.
Main Methods:
- Extracted 348 peer-reviewed articles from the Scopus database.
- Utilized CiteSpace and VOSviewer for scientometric analysis.
- Analyzed citation frequency, author collaboration, and publication trends.
Main Results:
- Identified leading authors, highly-cited journals, and international research collaborations.
- Co-occurrence networks highlighted prominent research topics.
- Bibliometric coupling revealed knowledge advancements in the field.
Conclusions:
- Deep learning methods are increasingly prevalent in MRI-based brain tumor classification.
- The study identifies current trends, research gaps, and future research avenues.
