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The human brain, while incredibly remarkable, is also fragile and susceptible to various issues, including brain tumors and neurological disorders. Fortunately, advanced techniques enable us to detect these anomalies. This research paper focuses on efficiently detecting and analyzing brain tumor types from magnetic resonance imaging (MRI) images. The approach involves creating a deep learning model using EfficientNet convolutional neural network and employing ImageNet for data augmentation. This combination performs exceptionally well on grayscale MRI images from computed tomography scans, achieving a 99.8% accuracy across diverse image data. The model is resilient, working on limited hardware resources, and the web application functions well in low-bandwidth conditions. Besides tumor detection, it provides treatment suggestions, offering a comprehensive solution to various challenges. This project aims to be a sustainable solution, aiding doctors with consistently high accuracy and precision.

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Brain Tumor Detection and Classification in MRI Images Using Deep Learning

  • 会议论文

  • 发表日期：

    2025年

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  • K. Sukanya Varshini,
  • A. Jackulin Mahariba,
  • A. L. Amutha,
  • S. Priya,
  • G. Prabaharan

摘要

The human brain, while incredibly remarkable, is also fragile and susceptible to various issues, including brain tumors and neurological disorders. Fortunately, advanced techniques enable us to detect these anomalies. This research paper focuses on efficiently detecting and analyzing brain tumor types from magnetic resonance imaging (MRI) images. The approach involves creating a deep learning model using EfficientNet convolutional neural network and employing ImageNet for data augmentation. This combination performs exceptionally well on grayscale MRI images from computed tomography scans, achieving a 99.8% accuracy across diverse image data. The model is resilient, working on limited hardware resources, and the web application functions well in low-bandwidth conditions. Besides tumor detection, it provides treatment suggestions, offering a comprehensive solution to various challenges. This project aims to be a sustainable solution, aiding doctors with consistently high accuracy and precision.