| --- |
| title: Alzheimer Classification |
| emoji: 📈 |
| colorFrom: indigo |
| colorTo: indigo |
| sdk: gradio |
| sdk_version: 4.36.1 |
| app_file: app.py |
| pinned: false |
| license: apache-2.0 |
| --- |
| # Alzheimer MRI Classification |
|
|
| This repository contains a Gradio application for classifying Alzheimer's disease stages from MRI images using a fine-tuned ResNet50 model. The application is deployed on Hugging Face Spaces. |
|
|
| ## Table of Contents |
| - [Introduction](#introduction) |
| - [Model Details](#model-details) |
| - [Setup](#setup) |
| - [Usage](#usage) |
| - [Contributing](#contributing) |
|
|
| ## Introduction |
| This application uses a convolutional neural network (ResNet50) to classify MRI images into one of four stages of Alzheimer's disease: |
| - Mild Demented |
| - Moderate Demented |
| - Non-Demented |
| - Very Mild Demented |
|
|
| The model is fine-tuned on a custom dataset and can be accessed through a user-friendly web interface powered by Gradio. |
|
|
| ## Model Details |
| The model architecture is based on ResNet50, with the final fully connected layer adjusted to output predictions for 4 classes. The model is trained using PyTorch and fine-tuned on a dataset of MRI images. |
|
|
| ## Setup |
| To run the application 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. Ensure you have the model file (`alzheimer_model_resnet50.pth`) in the root directory of the project. You can download it from [Hugging Face Hub](https://huggingface.co/your_username/alzheimer_model_resnet50). |
|
|
| 4. Run the application: |
| ```bash |
| python app.py |
| ``` |
| |
| 5. The Gradio interface will launch and can be accessed in your web browser at `http://127.0.0.1:7860`. |
|
|
| ## Usage |
| Once the application is running, you can upload an MRI image through the web interface and get the predicted classification. |
|
|
| ### Example Usage |
| 1. Open the application in your browser. |
| 2. Click on "Upload an MRI Image" to upload an image. |
| 3. The application will display the predicted classification for the uploaded image. |
|
|
|
|
| ## Contributing |
| Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request. |
|
|
|
|
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
|
|
