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Research Article

A Review of Deep Learning Methods for Brain Tumor Detection

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  • @ARTICLE{10.4108/eetel.8441,
        author={Shuaichao Wen},
        title={A Review of Deep Learning Methods for Brain Tumor Detection},
        journal={EAI Endorsed Transactions on e-Learning},
        volume={11},
        number={1},
        publisher={EAI},
        journal_a={EL},
        year={2025},
        month={4},
        keywords={Deep Learning, Disease Diagnosis, Brain Tumour, Medical Image},
        doi={10.4108/eetel.8441}
    }
    
  • Shuaichao Wen
    Year: 2025
    A Review of Deep Learning Methods for Brain Tumor Detection
    EL
    EAI
    DOI: 10.4108/eetel.8441
Shuaichao Wen1,*
  • 1: Henan Polytechnic University
*Contact email: wscx@home.hpu.edu.cn

Abstract

A brain tumor is a serious neurological condition caused by the growth of abnormal cells in various regions of the brain, leading to a variety of health issues. Although the specific causes of brain tumors are not yet fully understood, known risk factors include genetic predisposition, ionizing radiation, viral infections, and exposure to certain chemicals. With the advancement of deep learning technology, computer-aided diagnosis systems can offer crucial support for the early diagnosis of brain tumors. Brain tumor image classification using deep learning has emerged as a prominent area of research. This article begins by summarizing the publicly available datasets frequently utilized in brain tumor classification tasks. It then provides an overview of the models commonly applied for diagnosing brain tumors. Following this, the paper reviews the advancements made in the field of brain tumor classification research to date. Finally, it highlights the future trends and challenges in brain tumor classification.

Keywords
Deep Learning, Disease Diagnosis, Brain Tumour, Medical Image
Received
2025-04-11
Accepted
2025-04-11
Published
2025-04-11
Publisher
EAI
http://dx.doi.org/10.4108/eetel.8441

Copyright © 2025 S. Wen, licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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