Brain Tumor Detection using CNN
Globally, over 700,000 people are diagnosed with a brain tumor each year. Early detection is crucial as it significantly improves treatment outcomes and survival rates. Timely intervention helps in managing symptoms and reducing the tumor's impact on the brain.
This brain-tumor-classification model, powered by a convolutional neural network (CNN), tries to accurately differentiate between tumor and non-tumor images using brain MRI images. By analyzing these medical images with high precision, it aids in early detection and timely medical intervention, enhancing patient outcomes.
The datataset used is the kaggle's Brain MRI dataset, which consists of mri images of brain with and without tumor.
Dataset Source Link: kaggle dataset
- Clone the repository
git clone https://github.com/priyanshudutta04/Brain-Tumor-Detection.git
- Install dependencies
pip install -r requirements.txt
- Run the Model
jupyter notebook Model_Training.ipynb
Note: If GPU is available install cuda toolkit and cuDNN for faster execution
Contributions are welcome! If you have ideas for improving the model or adding new features, please feel free to fork the repository and submit a pull request.
This brain-tumor-classification model is intended for educational and demonstration purposes only. Always seek the advice of a qualified healthcare professional before making any medical decisions or starting any treatment. This model should not be used as a basis for medical diagnosis or treatment and the creator of this model are not responsible for any decisions made based on its output.
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