Mounika Gunde, Kollem Sreedhar, Samala Srinivas
Department of ECE, SR University, Warangal, Telangana, India, 506371.
Neuroradiology. 2025 Jul 18. doi: 10.1007/s00234-025-03685-z.
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.
This study aimed to investigate brain tumor classification based on magnetic resonance imaging (MRI), using scientometric approaches, from 2015 to 2024.
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.
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.
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.
磁共振成像（MRI）是一种广泛用于评估异常组织的非侵入性方法，尤其是在脑部。虽然许多研究已经使用MRI对脑肿瘤进行分类，但全面的科学计量分析仍然有限。
本研究旨在使用科学计量方法，对2015年至2024年基于磁共振成像（MRI）的脑肿瘤分类进行调查。
从Scopus数据库中提取了348篇同行评审文章。使用CiteSpace和VOSviewer等工具分析关键指标，包括被引频次、作者合作和发表趋势。
分析揭示了顶尖作者、高被引期刊和国际合作情况。共现网络确定了热门研究主题，文献耦合揭示了该领域的知识进展。
深度学习方法在脑肿瘤分类研究中的应用越来越多。本研究概述了当前趋势，揭示了研究差距，并为基于MRI的脑肿瘤分类领域的研究人员提出了未来方向。
