Classify Brain MRI
Upload a brain MRI scan image to classify the type of tumor present, or try with a random sample.
Upload MRI Image
Drag & drop your file here or click to browse
Supports: JPG, PNG, DICOM
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Batch Processing
Upload multiple MRI scans for batch classification
Drag & drop multiple files or click to browse
Classification Results
Detailed Analysis
Model Explanation
Heatmap showing regions that influenced the classification decision
About Our Model
Our brain tumor classification system uses a Convolutional Neural Network (CNN) trained on MRI scans to identify four different types of brain conditions.
How It Works
Our system uses a deep learning model trained on thousands of MRI scans to accurately classify brain tumors.
CNN Architecture
Model Architecture
Our brain tumor classification system uses a Convolutional Neural Network (CNN) with multiple convolutional layers, batch normalization, and dropout for regularization. The model was trained on a dataset of brain MRI scans to classify tumors into four categories.
Training Process
The model was trained using the Adam optimizer with a learning rate of 0.0005. We used categorical cross-entropy as the loss function and implemented data augmentation techniques to improve generalization.
Data Augmentation
- Random rotations (±15°)
- Random horizontal & vertical flips
- Brightness & contrast adjustments
- Zoom range: 0.9 to 1.1
Training Parameters
- Batch size: 32
- Epochs: 50 with early stopping
- Validation split: 20%
- Test split: 10%
Model Performance
Our model achieves state-of-the-art performance on brain tumor classification tasks, with high accuracy across all tumor types.