Github repository for Software Engineering Group 23 (2021)
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Github repository for Software Engineering Group 23 (2021)
COVID-19 has brought the world into a deep financial and health crisis. With the number of cases rising daily, it becomes increasingly difficult to test and diagnose the virus efficiently and effectively. The purpose of this system is to make the diagnosis of COVID-19 a lot quicker and easier. This can be achieved with the help of modern technologies such as machine learning and deep learning. A number of datasets have been made public over the duration of this pandemic. These datasets consist of various X-rays, some of which test positive for COVID-19 and others negative. This project uses a concatenation of some of these datasets to train a Convolutional Neural Networks model. Once trained, this model has the potential to predict the presence of COVID-19 on X-ray scans.
- Microsoft Azure
- Python
- TensorFlow
- Keras
- sciKit Learn
To get a local copy up and running follow these simple steps.
You need to have access to Microsoft Azure. Install the following packages:
pip install tensorflow
pip install sklearn
pip install azure-storage-blob
pip install requests
pip install python-dotenv
- Clone the repo
git clone https://github.com/hamza-mughees/Microsoft-Detecting-COVID-19.git
The intention of this project is to detect the presence of COVID-19 in X-Ray images of lungs.
It works by passing an X-Ray image of lungs through our trained convolutional neural network, them the CNN will output a percentage with the probability of the diagnosis being positive. To run a diagnosis on your image, download the api-call.py file and insert the path to the image you wish to diagnose. Then run the script and the API will call the model and return a diagnosis of the image
- 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
Distributed under the MIT License.
Hamza - [email protected]
Jason - [email protected]
Masanari - [email protected]
Sarah - [email protected]
Sean - [email protected]