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This thesis uses improved Brain Magnetic Resonance Imaging (MRI) to solve the urgent problem of brain tumor detection and classification. Since tumors are the second most common cause of cancer-related deaths, timely and precise diagnosis is essential to patient survival. At the moment, manual MRI image analysis takes a long time and is error-prone. A viable remedy is to use Deep Learning architectures such as CNN and VGG 16 Transfer learning. These models identify tumors before they appear in photos, allowing for prompt treatment and possibly even saving lives. This work aims to design and evaluate a deep learning-based system for rapid and accurate brain tumor diagnosis from MRI scans, thereby improving therapeutic outcomes by overcoming shortcomings in existing manual methods.

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Brain Tumor Detection Using MRI and Deep Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Kajal Singh,
  • Vineet Singh,
  • Bramah Hazela,
  • Shikha Singh

摘要

This thesis uses improved Brain Magnetic Resonance Imaging (MRI) to solve the urgent problem of brain tumor detection and classification. Since tumors are the second most common cause of cancer-related deaths, timely and precise diagnosis is essential to patient survival. At the moment, manual MRI image analysis takes a long time and is error-prone. A viable remedy is to use Deep Learning architectures such as CNN and VGG 16 Transfer learning. These models identify tumors before they appear in photos, allowing for prompt treatment and possibly even saving lives. This work aims to design and evaluate a deep learning-based system for rapid and accurate brain tumor diagnosis from MRI scans, thereby improving therapeutic outcomes by overcoming shortcomings in existing manual methods.