Computer Aided Diagnosis of Obesity Disorder based on Custom CNN

Based on Custom CNN architecture. The work uses proprietory image dataset obtained from SRM Hospital for training and validation. It uses Flask webapp by mtobeiyf to deploy the Keras Model https://github.com/mtobeiyf/keras-flask-deploy-webapp.

Getting started in 10 minutes

Clone this repo
Install requirements
Run the script
Check http://localhost:5000
Done! 🎉

👇Screenshot:

Local Installation

Clone the repo

$ git clone https://github.com/palanithanarajk/obesity-detection-app.git

Install requirements

$ pip install -r requirements.txt

Make sure you have the following installed:

tensorflow
keras
flask
pillow
h5py
gevent
gunicorn

Run with Python

Python 2.7 or 3.5+ are supported and tested.

$ python app.py

Play

Open http://localhost:5000 and have fun. 😃

Analyze

Diagnosis Result

UI Modification

Modify files in templates and static directory.

index.html for the UI and main.js for all the behaviors

Deployment

To deploy it for public use, you need to have a public linux server.

Run the app

Run the script and hide it in background with tmux or screen.

$ python app.py

You can also use gunicorn instead of gevent

$ gunicorn -b 127.0.0.1:5000 app:app

More deployment options, check here

Set up Nginx

To redirect the traffic to your local app. Configure your Nginx .conf file.

server {
    listen  80;

    client_max_body_size 20M;

    location / {
        proxy_pass http://127.0.0.1:5000;
    }
}

A Heroku App Deployment using this code

Try this live AI Demo https://dl-obesity-detection-ir.herokuapp.com/

More resources

Check Siraj's "How to Deploy a Keras Model to Production" video. The corresponding repo.

Building a simple Keras + deep learning REST API