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This paper presents a comprehensive approach for brain tumor detection by leveraging convolutional neural networks (CNNs) in conjunction with thermal analysis. The integration of CNNs with thermal data provides a novel and complementary perspective on brain tumor detection, enhancing the accuracy and reliability of diagnosis. The thermal analysis involves the use of infrared (IR) thermography to capture the heat distribution within the brain. By quantifying the temperature variations, the study aims to detect abnormal thermal patterns associated with brain tumors. These thermal patterns can provide valuable insights into tumor physiology, potentially enhancing the diagnostic process. This innovative approach has the potential to significantly impact the field of neuroimaging and brain tumor diagnosis, leading to earlier detection and more effective treatment strategies. The integration of CNN-VGG-16 and thermo-colored analysis holds the promise for improvement of the overall understanding of brain tumors and their heterogeneity. The proposed model outperforms with 98.58% accuracy than existing models.

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A Novel and Intelligent Neuroimage Analysis for Tumor Classification with Thermo-Color Segmentation Using VGG-16

  • 会议论文

  • 发表日期：

    2025年

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  • Krushna Chandra Mohapatra,
  • Sai Arpit Mund,
  • Aman Kumar Senapati,
  • Shalini Biswal,
  • Ram Chandra Barik,
  • Debendra Muduli

摘要

This paper presents a comprehensive approach for brain tumor detection by leveraging convolutional neural networks (CNNs) in conjunction with thermal analysis. The integration of CNNs with thermal data provides a novel and complementary perspective on brain tumor detection, enhancing the accuracy and reliability of diagnosis. The thermal analysis involves the use of infrared (IR) thermography to capture the heat distribution within the brain. By quantifying the temperature variations, the study aims to detect abnormal thermal patterns associated with brain tumors. These thermal patterns can provide valuable insights into tumor physiology, potentially enhancing the diagnostic process. This innovative approach has the potential to significantly impact the field of neuroimaging and brain tumor diagnosis, leading to earlier detection and more effective treatment strategies. The integration of CNN-VGG-16 and thermo-colored analysis holds the promise for improvement of the overall understanding of brain tumors and their heterogeneity. The proposed model outperforms with 98.58% accuracy than existing models.