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Detection of brain tumors in their early stages is crucial for improving patient outcomes and treatment efficacy. Many related pragmatic clinical tools and various machine learning models are made for the effective diagnosis of patients, but they still provide low accuracy scores. Therefore, the need to develop a more precise model for better screening of patients to detect tumors still exists. There has been a growing interest in using methodologies like Convolutional Neural Networks (CNNs) in medical imaging in recent years. By applying these advanced techniques alongside various machine learning (ML) algorithms, this study aims to discover the model which yields the best accuracy for detecting brain tumors.

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Deep Learning Approaches for Brain Tumor Detection Using Hyperspectral Imaging

  • 会议论文

  • 发表日期：

    2025年

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  • Garima Jaiswal,
  • Anika Sharma,
  • Mannat Aggarwal,
  • Eshit Saini

摘要

Detection of brain tumors in their early stages is crucial for improving patient outcomes and treatment efficacy. Many related pragmatic clinical tools and various machine learning models are made for the effective diagnosis of patients, but they still provide low accuracy scores. Therefore, the need to develop a more precise model for better screening of patients to detect tumors still exists. There has been a growing interest in using methodologies like Convolutional Neural Networks (CNNs) in medical imaging in recent years. By applying these advanced techniques alongside various machine learning (ML) algorithms, this study aims to discover the model which yields the best accuracy for detecting brain tumors.