Brain MRI Image Classification using 3D DenseNet121 and Self Attention

Overview

This repository contains a Jupyter Notebook file that demonstrates the process of classifying brain MRI images into Alzheimer's Disease (AD), Cognitive Normal (CN), and Mild Cognitive Impairment (MCI) using a 3D DenseNet121 architecture with self-attention mechanism. The dataset used for training and evaluation is the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.

Prerequisites

Before running the notebook, ensure you have the following dependencies installed:

• scikit-learn
• TensorFlow
• Keras

You can install these libraries using the following:

pip install scikit-learn tensorflow keras

Dataset

The ADNI dataset is used for training and testing the model. Please download the dataset and place it in the appropriate directory. You can download the ADNI dataset from ADNI's official website.

Running the Notebook

1. Download the ADNI dataset and organize it in a directory.
2. Open the ipynb notebook using Jupyter Notebook or Jupyter Lab.
3. Execute each cell in the notebook sequentially.

Happy coding!