Deep Learning for Medical Imaging: Brain Tumor Classification
Accurate brain tumor diagnosis is critical for treatment planning. Our research developed a CNN-based system achieving 96% accuracy in classifying brain tumors from MRI scans, with a research paper published in IEEE Access.
Medical Context
Brain tumors require rapid, accurate diagnosis to guide treatment decisions. Traditional manual analysis is:
- Time-consuming (30-45 minutes per case)
- Subject to inter-observer variability
- Requires specialized expertise
- Prone to fatigue-related errors
Our Solution
We developed a dual-head neural network architecture based on EfficientNetB0 for multi-class brain tumor classification from MRI images.
Model Architecture
Input (MRI Scan)
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EfficientNetB0 (Backbone)
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+-- Detection Head (Tumor/No Tumor)
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+-- Classification Head (4 Tumor Types)
The dual-head approach first detects tumor presence, then classifies into glioma, meningioma, pituitary, or no tumor.
Results
- 96% overall accuracy
- 93.2% sensitivity for tumor detection
- 98.1% specificity
- Processing time: <2 seconds per scan
Dataset & Training
- 7,023 MRI images from public medical imaging datasets
- Data augmentation: rotation, flip, zoom, brightness adjustment
- 5-fold cross-validation for robust evaluation
- Training time: 12 hours on NVIDIA V100 GPU
Key Innovations
- Attention mechanisms to focus on tumor regions
- Transfer learning from ImageNet pre-trained weights
- Class balancing using weighted loss functions
- Explainable AI using Grad-CAM visualization
Clinical Impact
The system assists radiologists by:
- Providing second-opinion validation
- Highlighting regions of interest
- Reducing diagnosis time by 70%
- Standardizing classification criteria
Future Work
We're working on expanding to other neurological conditions and integrating with hospital PACS systems for seamless clinical workflow integration.
Technologies: Python, TensorFlow, Keras, OpenCV, NumPy, Grad-CAM