Armanx200 / Brain-Tumor-Detector

🧠 A deep learning model that classifies brain images as either having a tumor or not.

# 🧠 Brain Tumor Detection with Deep Learning

Welcome to the **Brain Tumor Detection** project! This repository contains a deep learning model that classifies brain images as either having a tumor or not. Dive into the power of convolutional neural networks (CNNs) and see how they can be used in medical imaging! 🚀

## 📁 Project Structure

```plaintext
brain-tumor-detection/
│
├── brain_tumor_dataset/
│   ├── no/         # Images without tumors
│   └── yes/        # Images with tumors
│
├── Brain-tumor-detector.py        # Model training and evaluation
└── Brain-tumor-probability.py     # Tumor probability prediction for a specific image

📜 Description

This project uses a CNN model to classify brain images into two categories:

• No tumor (stored in brain_tumor_dataset/no)
• Tumor (stored in brain_tumor_dataset/yes)

🛠️ Getting Started

1. Clone the repository:

   git clone https://github.com/Armanx200/Brain-tumor-detector.git
   cd Brain-tumor-detector

2. Install dependencies:

   pip install tensorflow opencv-python scikit-learn

3. Download the dataset:

   • Ensure you have the brain tumor dataset in the correct structure as shown above.

🚀 Training the Model

Run the Brain-tumor-detector.py script to train the model:

python Brain-tumor-detector.py

This script:

• Loads and preprocesses the dataset.
• Splits the data into training and testing sets.
• Builds and trains a CNN model.
• Evaluates the model and displays the test accuracy.

🔍 Predicting Tumor Probability

To predict the probability of a specific image having a tumor, use the Brain-tumor-probability.py script:

1. Ensure the model is trained and saved as brain_tumor_model.h5.

2. Run the prediction script:

   python Brain-tumor-probability.py

   This script will:

   • Load the trained model.
   • Preprocess the specified image.
   • Output the probability of the image being a tumor.

📊 Model Performance

The model achieves impressive accuracy on the test set. See the script output for detailed performance metrics.

🛠️ Tools and Technologies

• TensorFlow/Keras: For building and training the neural network.
• OpenCV: For image processing.
• Scikit-learn: For data splitting and preprocessing.

📸 Sample Results

Below is an example of how the model predicts the probability of an image being a tumor:

Probability of the image being a tumor: 95.44%

🤝 Contributing

Contributions are welcome! Please create a pull request or open an issue for any improvements or bug fixes.

📜 License

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

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Developed with ❤️ by Armanx200