udaylunawat/Covid-19-Radiology

Covid-19 Radiology

Detects Covid-19 and Pneumonia using Coronal Xray images & Deep Learning.

Table of Contents
Motivation
Instructions
Getting Started
- Installation
Usage
Demo
Technologies Used
Roadmap
Contributing
License
Contact

Motivation

Keeping track of the Pandemic data, from a personal viewpoint.
Experimenting with Radiology data & Coronal X-ray images.

Instructions

Run in Google Colab

View source on GitHub

Download notebook

Demo

Link: Deploy on colab in 2 mins

Directory Tree

├── data
│   ├── sample_images                 <- Sample images for inference
│   ├── 0_raw                         <- The original, immutable data dump.
│   ├── 1_external                    <- Data from third party sources.
│   ├── 2_interim                     <- Intermediate data that has been transformed.
│   └── 3_processed                   <- The final, canonical data sets for modeling.
│
├── notebooks                         <- Jupyter notebooks. Naming convention is a number (for ordering),
│                                        the creator's initials, and a short `-` delimited description, e.g.
│                         `              1.0-jqp-initial-data-exploration`.
│
├── output
│   ├── models                        <- Trained and serialized models, model predictions, or model summaries
│   │   ├── snapshots                 <- Saving training snapshots.
│   │   ├── inference                 <- Converted trained model to an inference model.
│   │   └── TrainingOutput            <- Output logs
│   └── figures                       <- Generated graphics and figures to be used in reporting
│
├── src                               <- Source code for use in this project.
│   ├── __init__.py                   <- Makes src a Python module
│   │
│   ├── data                          <- Scripts to download or generate data
│   │   ├── make_dataset.py
│   │   └── preprocess.py    
│   │
│   ├── models                        <- Scripts to train models and then use trained models to make
│   │   │                                predictions
│   │   ├── predict_model.py
│   │   ├── train_model.py
│   │   └── inference.py
│   │
│   └── visualization                 <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
├── utils                             <- Utility scripts for Streamlit etc.
├── serve                             <- HTTP API for serving predictions using Streamlit
│   ├── Dockerfile                    <- Dockerfile for HTTP API
│   ├── Pipfile                       <- The Pipfile for reproducing the serving environment
│   └── app.py                        <- The entry point of the HTTP API using Streamlit app
│
├── .dockerignore                     <- Docker ignore
├── .gitignore                        <- GitHub's excellent Python .gitignore customized for this project
├── app.yaml                          <- contains configuration that is applied to each container started
│                                        for that service
├── config.py                         <- Global configuration variables
├── LICENSE                           <- Your project's license.
├── Makefile                          <- Makefile with commands like `make data` or `make train`
├── README.md                         <- The top-level README for developers using this project.
├── tox.ini                           <- tox file with settings for running tox; see tox.readthedocs.io
├── requirements.txt                  <- The requirements file for reproducing the analysis environment, e.g.
│                                        generated with `pip freeze > requirements.txt`
└── setup.py                          <- makes project pip installable (pip install -e .) so src can be imported

Future To Do

Work with Xray of any orientation other than coronal.

Bug / Feature Request

If you find a bug (the website couldn't handle the query and / or gave undesired results), kindly open an issue here by including your search query and the expected result.

If you'd like to request a new function, feel free to do so by opening an issue here. Please include sample queries and their corresponding results.

Technologies Used

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

Fork the Project
Create your Feature Branch (git checkout -b feature/AmazingFeature)
Commit your Changes (git commit -m 'Add some AmazingFeature')
Push to the Branch (git push origin feature/AmazingFeature)
Open a Pull Request

License

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Credits

Project based on the cookiecutter data science project template. #cookiecutterdatascience

Made with 💙 for India

udaylunawat / Covid-19-Radiology