MRI Diagnosis using CNN and Transfer Learning

This repository contains the code for a graduate project focused on MRI Diagnosis. The project utilizes Convolutional Neural Networks (CNN) and Transfer Learning techniques to analyze MRI scans.

Project Overview

The project was completed as part of a master's course and is implemented using Jupyter Notebook. The database for the project was created using MRI scans.

Technologies Used

• Google Colab: The project was developed and run entirely in Google Colab, a cloud-based Jupyter notebook environment that allows for free GPU use.

• TensorFlow: TensorFlow, an open-source platform for machine learning, was used to build and train the CNN.

• Scikit-Learn (sklearn): Scikit-Learn, a machine learning library for Python, was used for various tasks such as model evaluation and data preprocessing.

• Xception: The Xception model, a deep convolutional neural network architecture, was used as a pre-trained model for transfer learning.

Getting Started

To run the project, you will need to have Jupyter Notebook installed. You can then clone this repository and open the .ipynb file in Jupyter Notebook.

Contributing

Contributions are welcome! Please feel free to submit a pull request.

License

This project is licensed under the terms of the MIT license.