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BrainView: A cloud-based deep learning system for brain image segmentation, tumor detection and visualization
1Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX, USA.
Biomedical Journal
|May 23, 2025
Summary
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.
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.
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