Our project is a work of three teammates, manchened at the end of this document. We've used a new method Called Hybrid Quantum-Classical Model which is a mixe between Quantum Circuits used as Neural Network and a Classical Neural Network, Our Solution is about Brain Cancer Classification that means Classifying different Tumor types from MRI pictures so a Convolutional Neural Network is a normal method but here we did it with an Hybrid Quantum ConvNet as designed above:
- Retrieve patient MRI image
- Process the image
- predict the result and display it
- Update the database
This project is interpreted/tested on Ubuntu 20.04.3 LTS using python3 (V 3.8.3)
- Clone this repository:
git clone "https://github.com/ggirlk/Brain_Cancer_Classification.git" - Access deployment directory:
cd deployment - Run the command:
python manage.py runserver - Browse your MRI and wait for the result
- Pennylane: Quantum Machine Learning Tool.
- Tensorflow: Classical Machine Learning Tool.
- OpenCv (cv2): Image Processing Tool.
- Pandas: Data Manipulation Tool.
- Numpy: Matrix Manipulation Tool
- Scikit-Learn (sklearn): Classical Machine Learning Tool.
- Matplotlib: Plotting Tool.
- Django: Python Web Framework.
No known bugs at this time.
- CNN
- QCNN
- Pennylane tutorial
- Pennylane
- Qiskit
- Quantum Computing Concepts – Entanglement
Public Domain. No copy write protection.
By Khouloud, Ghofrane and Mouhamed Software engineers at HolbertonSchool®️