# Fine-Tuning ResNet50 for Alzheimer's MRI Classification

This repository contains a Jupyter Notebook for fine-tuning a ResNet50 model to classify Alzheimer's disease stages from MRI images. The notebook uses PyTorch and the dataset is loaded from the Hugging Face Datasets library.

## Table of Contents
- [Introduction](#introduction)
- [Dataset](#dataset)
- [Model Architecture](#model-architecture)
- [Setup](#setup)
- [Training](#training)
- [Evaluation](#evaluation)
- [Usage](#usage)
- [Results](#results)
- [Contributing](#contributing)
- [License](#license)

## Introduction
This notebook fine-tunes a pre-trained ResNet50 model to classify MRI images into one of four stages of Alzheimer's disease:
- Mild Demented
- Moderate Demented
- Non-Demented
- Very Mild Demented

## Dataset
The dataset used is [Falah/Alzheimer_MRI](https://huggingface.co/datasets/Falah/Alzheimer_MRI) from the Hugging Face Datasets library. It consists of MRI images categorized into the four stages of Alzheimer's disease.

## Model Architecture
The model architecture is based on ResNet50. The final fully connected layer is modified to output predictions for 4 classes.

## Setup
To run the notebook locally, follow these steps:

1. Clone the repository:
    ```bash
    git clone https://github.com/your_username/alzheimer_mri_classification.git
    cd alzheimer_mri_classification
    ```
2. Install the required dependencies:
    ```bash
    pip install -r requirements.txt
    ```
3. Open the notebook:
    ```bash
    jupyter notebook fine-tuning.ipynb
    ```
## Training
The notebook includes sections for:
- Loading and preprocessing the dataset
- Defining the model architecture
- Setting up the training loop with a learning rate scheduler and optimizer
- Training the model for a specified number of epochs
- Saving the trained model weights

## Evaluation
The notebook includes a section for evaluating the trained model on the validation set. It calculates and prints the validation loss and accuracy.

## Usage
Once trained, the model can be saved and used for inference on new MRI images. The trained model weights are saved as alzheimer_model_resnet50.pth.

##  Load the model architecture and weights
     ```python
    model = models.resnet50(weights=None)
    model.fc = nn.Linear(model.fc.in_features, 4)
    model.load_state_dict(torch.load("alzheimer_model_resnet50.pth", map_location=torch.device('cpu')))
    model.eval()
    ```
## Results
The model achieved an accuracy of 95.9375% on the validation set.
## Contributing
Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request.