Spaces:
Runtime error
Runtime error
File size: 3,485 Bytes
50a1853 e05dd7f 50a1853 e05dd7f 50a1853 e05dd7f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 |
---
title: BRAIN_TUMOR_DEDECTOR
app_file: app.py
sdk: gradio
sdk_version: 5.6.0
---
# 🧠 Brain Tumor MRI Classification
[](https://www.python.org/)
[](https://www.tensorflow.org/)
[](https://streamlit.io/)
A deep learning project that uses transfer learning and custom CNN architectures to classify brain tumors from MRI scans into four categories: Glioma, Meningioma, Pituitary, and No Tumor.
## 🎯 Project Overview
This project implements two different deep learning approaches:
1. **Transfer Learning with Xception**: Leveraging a pre-trained model for enhanced accuracy
2. **Custom CNN**: A dedicated convolutional neural network built from scratch
Both models are deployed through a user-friendly Streamlit web interface that provides:
- Real-time tumor classification
- Confidence scores
- Saliency maps for model interpretability
- AI-generated explanations of the model's focus areas
## 📊 Model Architecture & Performance
### Transfer Learning Model (Xception)
- Pre-trained on ImageNet dataset
- 36 convolutional layers
- 21 million parameters
- Features:
- Max pooling
- Dropout layers for regularization
- Softmax activation for classification
- Performance metrics:
- High accuracy on test set
- Robust against overfitting
### Custom CNN Model
- 4 convolutional layers
- 4.7 million parameters
- Architecture:
- Multiple Conv2D layers with ReLU activation
- MaxPooling2D layers
- Dropout for regularization
- Dense layers with L2 regularization
- Softmax output layer
## 🛠️ Installation
```bash
# Clone the repository
git clone https://github.com/stonewerner/brain-tumor-ML.git
cd brain-tumor-ML
# Install required packages
pip install -r requirements.txt
```
## 📦 Dependencies
- tensorflow >= 2.0
- streamlit
- numpy
- pandas
- pillow
- plotly
- opencv-python
- google-generativeai
- python-dotenv
## 🚀 Usage
### Running the Web App
```bash
streamlit run app.py
```
## 🖥️ Web Interface Features
1. **Image Upload**: Support for jpg, jpeg, and png formats
2. **Model Selection**: Choose between Transfer Learning and Custom CNN
3. **Visualization**:
- Original MRI scan
- Saliency map highlighting model focus areas
4. **Results Display**:
- Predicted tumor type
- Confidence scores
- Interactive probability chart
- AI-generated explanation of the classification
## 📈 Data Processing
The project includes robust data handling:
- Image preprocessing and augmentation
- Brightness adjustment for training data
- Proper train/validation/test splits
- Standardized image sizing
## 🔍 Model Interpretability
- **Saliency Maps**: Visual explanation of model decisions
- **Region Focus**: Highlights critical areas in MRI scans
- **AI Explanations**: Generated using Google's Gemini model
- **Confidence Metrics**: Probability distribution across classes
## 👥 Contributing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## 📧 Contact
Stone Werner - stonewerner.com
Project Link: [https://github.com/stonewerner/brain-tumor-ML](https://github.com/stonewerner/brain-tumor-ML)
|