Deep Learning for Medical Imaging: Brain Tumor Classification
Accurate brain tumor diagnosis is critical for treatment planning. This research developed a CNN-based system achieving 93.2% accuracy in classifying brain tumors from MRI scans. The manuscript is currently under review at Elsevier Results in Engineering.
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
- 93.2% multi-class classification accuracy
- Simultaneous tumor type and grade prediction in a single architecture
- Compact model: ~4.0M parameters, ~15ms inference
- Efficient enough for practical deployment pathways
Dataset & Training
- MRI images from public medical imaging datasets
- Data augmentation: rotation, flip, zoom, brightness adjustment
- Cross-validation for robust evaluation
- Transfer learning from ImageNet pre-trained weights
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 is designed to assist radiologists by:
- Providing second-opinion validation
- Highlighting regions of interest
- Reducing manual review time
- 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