ggirlk / brain_cancer_classification

Portfolio Project: Brain Cancer Classification with QML <🧠|1>

🤖 Intro

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:

👉 Process:

• Retrieve patient MRI image
• Process the image
• predict the result and display it
• Update the database

👉 Environment

This project is interpreted/tested on Ubuntu 20.04.3 LTS using python3 (V 3.8.3)

👉 Installation

• 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

👉 Technologies

Modeling:

• 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.

Web App

• Django: Python Web Framework.

👉 Bugs

No known bugs at this time.

👉 Resources

- CNN
- QCNN
- Pennylane tutorial
- Pennylane
- Qiskit
- Quantum Computing Concepts – Entanglement

License

Public Domain. No copy write protection.

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By Khouloud, Ghofrane and Mouhamed Software engineers at HolbertonSchool®️