This project is designed to classify Alzheimer's disease stages based on CT scan images. The core of the classification model is built using Keras (TensorFlow backend), with a Flask API to handle image processing and classification requests. The frontend is built using React to provide a user-friendly interface for uploading images and viewing results.
- Image Classification: Classifies Alzheimer's disease into different stages (e.g., mild, moderate, severe) using CT scan images.
- Model Training: Built using Keras and TensorFlow, trained on a dataset of CT scan images.
- REST API: Flask API for handling image uploads, preprocessing, and model inference.
- Responsive UI: React frontend allows users to upload CT scan images and view classification results.
- Frontend: React.js
- Backend: Python Flask, Keras, TensorFlow
- Deep Learning: Keras (TensorFlow backend)
- Version Control: Git
To set up and run this project locally, follow these steps:
- Node.js (v14+)
- Python (v3.8+)
- Git
- TensorFlow (v2.0+)
-
Clone the repository:
git clone https://github.com/your-username/alzheimers-ct-classification.git cd alzheimers-ct-classification
-
Install backend dependencies:
cd backend pip install -r requirements.txt
-
Install frontend dependencies:
cd ../frontend npm install
-
Set up environment variables: Create a
.env
file in thebackend
directory and add the following variables:FLASK_APP=app.py MODEL_PATH=path_to_your_trained_model.h5
-
Run the backend server:
cd backend flask run
-
Run the React frontend: Open a new terminal, and run:
cd frontend npm start
-
View the app: Visit
http://localhost:3000
in your browser to interact with the Alzheimer's classification tool.
├── backend
│ ├── app.py
│ ├── model
│ │ └── alzheimers_classifier.py
│ ├── static
│ │ └── uploads
│ ├── templates
│ ├── requirements.txt
├── frontend
│ ├── src
│ │ ├── components
│ │ ├── pages
│ │ ├── App.js
│ ├── public
│ └── package.json
└── README.md