🧠 Brain Tumor Classification – Hybrid Deep Learning Model

A deep learning project for automatic brain tumor classification from MRI images using a hybrid model that combines the strengths of multiple architectures for improved accuracy and generalization.
This project was developed and trained in Google Colab, and can be run end-to-end in the provided .ipynb notebook.

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✨ Features

• 🏥 Medical Imaging Focus – Designed for classifying brain MRI scans into tumor categories.
• 🔬 Hybrid Deep Learning Architecture – Combines convolutional neural networks (CNNs) with additional machine learning layers for improved performance.
• 📊 Training and Evaluation – Includes preprocessing, model training, validation, and performance metrics.
• ☁ Google Colab Ready – Fully runnable without local setup.
• 📈 Visualization Tools – Plots for accuracy, loss curves, and confusion matrices.

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📂 Project Structure

brain-tumor-classification/
│
├── brain-tumor-classification-hybrid-model.ipynb   # Main Google Colab notebook
└── README.md                                       # Project documentation

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🚀 Getting Started

1. Open in Google Colab

Click the badge below to open the notebook in Colab:

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2. Dataset

You can use publicly available datasets such as:

• Brain MRI Images for Brain Tumor Detection (Kaggle)
• Figshare Brain Tumor Dataset

Place the dataset in the appropriate directory or mount Google Drive in Colab.

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3. Install Dependencies

If using Colab, dependencies are installed within the notebook automatically.

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4. Run the Notebook

Execute all cells in the brain-tumor-classification-hybrid-model.ipynb notebook to train and evaluate the model.

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🧠 Model Architecture

• Feature Extractor – A CNN backbone for capturing spatial features from MRI images.
• Hybrid Layer – Combines CNN output with a secondary model (e.g., Dense layers, classical ML classifier) for better classification accuracy.
• Output Layer – Softmax activation for multi-class classification.

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📊 Results

The notebook includes:

• Accuracy and loss curves over training epochs
• Confusion matrix for detailed class-wise performance
• Example predictions with true vs predicted labels

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📌 Future Improvements

• Experiment with different CNN backbones (ResNet, EfficientNet, etc.)
• Implement data augmentation for robustness
• Deploy the model via a web app (e.g., Streamlit or Flask)

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📄 License

This project is licensed under the MIT License – see the LICENSE file for details.

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🙌 Acknowledgements

• Dataset providers (Kaggle, Figshare, etc.)
• TensorFlow / PyTorch community
• Google Colab for cloud-based development

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Built to assist in medical image classification research and education.