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The recognition of brain tumors is a significant issue in healthcare. A brain tumor is an unusual tissue mass where cells multiply rapidly and uncontrollably. Image segmentation helps identify the tumor regions in the brain using MRI scans. Early detection of brain tumors is essential, which can be achieved with machine learning and deep learning algorithms. Our research utilized various deep learning methods, including VGG-16, ResNet-152, Inception-V3, Inception ResNet-V2, and a Custom convolutional neural network model to categorize brain tumors. The sample dataset for our research consisted of 1085 tumorous and 980 non-tumorous images from the Kaggle online database. Among all the models, VGG-16 performed the best and achieved 98% accuracy in classifying brain tumors.

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Recognition of Brain Tumors Using Deep Neural Network Models

  • 会议论文

  • 发表日期：

    2025年

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  • Rashmi Shivanandhuni,
  • B. Krishna,
  • Gulab Singh Chauhan,
  • K. Manasa,
  • Sallauddin Mohmmad,
  • Shabana

摘要

The recognition of brain tumors is a significant issue in healthcare. A brain tumor is an unusual tissue mass where cells multiply rapidly and uncontrollably. Image segmentation helps identify the tumor regions in the brain using MRI scans. Early detection of brain tumors is essential, which can be achieved with machine learning and deep learning algorithms. Our research utilized various deep learning methods, including VGG-16, ResNet-152, Inception-V3, Inception ResNet-V2, and a Custom convolutional neural network model to categorize brain tumors. The sample dataset for our research consisted of 1085 tumorous and 980 non-tumorous images from the Kaggle online database. Among all the models, VGG-16 performed the best and achieved 98% accuracy in classifying brain tumors.