This code is for the paper COVID-Net Assistant: A Deep Learning-Driven Virtual Assistant for Early COVID-19 Recommendation
Link to the paper
Figure 1: Overview of COVID-Net Assistant workflow.
COVID-Net Assistant is core part of COVID-Net, a global open source, open access initiative dedicated to accelerating advancement in machine learning to aid front-line healthcare workers and clinical institutions around the world fighting the continuing pandemic. Link to COVID-Net portal: here
Note: The COVID-Net models provided here are intended to be used as reference models that can be built upon and enhanced as new data becomes available. They are currently at a research stage and not yet intended as production-ready models (not meant for direct clinical diagnosis), and we are working continuously to improve them as new data becomes available. Please do not use COVID-Net for self-diagnosis and seek help from your local health authorities.
As the COVID-19 pandemic continues to put a significant burden on healthcare systems worldwide, there has been growing interest in finding inexpensive symptom pre-screening and recommendation methods to assist in efficiently using available medical resources such as PCR tests. In this study, we introduce the design of COVID-Net Assistant, an efficient virtual assistant designed to provide symptom prediction and recommendations for COVID-19 by analyzing users' cough recordings through deep convolutional neural networks. We explore a variety of highly customized, lightweight convolutional neural network architectures generated via machine-driven design exploration (which we refer to as COVID-Net Assistant neural networks) on the Covid19-Cough benchmark dataset. The Covid19-Cough dataset comprises 682 cough recordings from a COVID-19 positive cohort and 642 from a COVID-19 negative cohort. Among the 682 cough recordings labeled positive, 382 recordings were verified by PCR test. Our experimental results show promising, with the COVID-Net Assistant neural networks demonstrating robust predictive performance, achieving AUC scores of over 0.93, with the best score over 0.95 while being fast and efficient in inference. The COVID-Net Assistant models are made available in an open source manner through the COVID-Net open initiative and, while not a production-ready solution, we hope their availability acts as a good resource for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative solutions. © 2022 .
- Requirements to install on your system
- How to download and prepare the Covid19-Cough dataset
- Steps for training, evaluation and inference
- Results and links to pretrained models
The main requirements are listed below:
- Tensorflow 1.15
- librosa
- audiomentations
- keras
- Python 3.7
- Numpy
- Scikit-Learn
- Matplotlib
- ffmpeg
- ipywebrtc
Please use this script to insall all requirements
$ sudo apt install ffmpeg
$ pip3 install -r requirements.txt
$ jupyter nbextension enable --py widgetsnbextension
If there are any technical questions after the README, FAQ, and past/current issues have been read, please post an issue or contact:
@article{covidnet-assistant,
author = {Shi, Pengyuan and Wang, Yuetong and Abbasi, Saad and Wong, Alexander},
title = {COVID-Net Assistant: A Deep Learning-Driven Virtual Assistant for COVID-19 Symptom Prediction and Recommendation},
year = {2022},
doi = {10.48550/ARXIV.2211.11944},
url = {https://arxiv.org/abs/2211.11944},
}