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BrainView: A cloud-based deep learning system for brain image segmentation, tumor detection and visualization.

Partho Ghose1, Hasan M Jamil2

  • 1Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX, USA.

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Summary
This summary is machine-generated.

This study introduces BrainView, a deep learning platform for brain tumor detection and classification using MRI scans. Our models achieved high accuracy in classifying tumor types and segmenting tumors, aiding early diagnosis.

Keywords:
Brain tumorDeep learningImage classificationImage segmentationMagnetic resonance images (MRI)

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Brain tumors disrupt neural function and pose a life threat.
  • Early detection and classification of brain tumors are critical for patient outcomes.
  • Deep learning shows promise for brain tumor image analysis.

Purpose of the Study:

  • To develop and evaluate a deep learning platform, BrainView, for brain tumor detection and segmentation.
  • To classify brain tumor types using Magnetic Resonance Images (MRI).
  • To localize brain tumors through image segmentation.

Main Methods:

  • Utilized EfficientNetB7 pre-trained models for classification (DeepBrainNet) and segmentation (EffB7-UNet).
  • Applied deep learning techniques to analyze brain MRI scans.
  • Developed a cloud application framework using Flask and Flutter.

Main Results:

  • Achieved 99.96% accuracy in brain tumor classification.
  • Attained 92.734% accuracy in brain tumor segmentation.
  • Demonstrated the efficacy of EfficientNetB7-based models for brain tumor analysis.

Conclusions:

  • The BrainView platform demonstrates high performance in brain tumor detection and classification.
  • Deep learning models, particularly EfficientNetB7-based ones, are effective for analyzing brain MRI.
  • A cloud-based application can facilitate online access to these AI models for research and clinical use.