Yuki Wong, Eileen Lee Ming Su, Che Fai Yeong, William Holderbaum, Chenguang Yang
Abstract
Brain tumors pose a significant medical challenge, necessitating early detection and precise classification for effective treatment. This study aims to address this challenge by introducing an automated brain tumor classification system that utilizes deep learning (DL) and Magnetic Resonance Imaging (MRI) images.
Introduction
The classification of brain tumors from Magnetic Resonance Imaging (MRI) images using deep learning (DL) techniques is a crucial area of research in the medical field. The brain is a complex and extremely sensitive organ of the human body [1].
Methods
This section provides a detailed account of the methodology, encompassing data acquisition, preprocessing, architectural design, and performance evaluation of the deep learning model.
Results
This section presents the outcomes of the study, highlighting the effectiveness of the developed brain MRI image classification system. The results encompass detailed analyses of the dataset generation process, training and validation performance, classification accuracy, and the robustness of the model as evaluated on a separate test dataset.
Discussion
The implementation of our DL-based brain tumor classification model achieved an overall accuracy of 99.24%, significantly surpassing the results of previous studies, particularly those by M. Abu et al. [14] and Pillai et al. [15].
Conclusion
This study successfully developed a brain tumor classification system leveraging Python and the VGG16 network as a pretrained model, achieving an impressive overall classification accuracy of 99.24% on a dataset comprising glioma, meningioma, pituitary tumors, and normal MRI images.
Citation: Wong Y, Su ELM, Yeong CF, Holderbaum W, Yang C (2025) Brain tumor classification using MRI images and deep learning techniques. PLoS One 20(5): e0322624. https://doi.org/10.1371/journal.pone.0322624
Editor: Muhammad Ramzan, University of Sargodha, PAKISTAN
Received: October 24, 2023; Accepted: March 24, 2025; Published: May 9, 2025
Copyright: © 2025 Wong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its supporting information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
