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This work aimed to tackle the difficulty of precisely identifying brain malignancies in MRI images. The process of manual detection is both laborious and susceptible to mistakes, which could have an impact on the treatment of patients. A training was conducted on a wide range of MRI scans that exhibited different tumor features. Convolutional Neural Networks (CNNs) and activation methods were utilized to improve the accuracy. Our approach, which utilizes Convolutional Neural Networks (CNNs), achieved an accuracy of 97.94%. This is a substantial improvement compared to previous cutting-edge results. It signifies a significant progress in the field of automatic brain tumor detection and establishes our technique as a highly dependable tool for practical MRI-based brain tumor detection in various scenarios.

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Brain Tumor Identification Utilizing Convolutional Deep Learning Approaches Based on MRI Imaging

  • 会议论文

  • 发表日期：

    2025年

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  • G. Elavel Visuvanathan,
  • Harisudha Kuresan,
  • A. Yogadharshini,
  • S. Sureya,
  • Vutukuri Surendra

摘要

This work aimed to tackle the difficulty of precisely identifying brain malignancies in MRI images. The process of manual detection is both laborious and susceptible to mistakes, which could have an impact on the treatment of patients. A training was conducted on a wide range of MRI scans that exhibited different tumor features. Convolutional Neural Networks (CNNs) and activation methods were utilized to improve the accuracy. Our approach, which utilizes Convolutional Neural Networks (CNNs), achieved an accuracy of 97.94%. This is a substantial improvement compared to previous cutting-edge results. It signifies a significant progress in the field of automatic brain tumor detection and establishes our technique as a highly dependable tool for practical MRI-based brain tumor detection in various scenarios.