Mini-COVIDNet: Efficient Lightweight Deep Neural Network for Ultrasound Based Point-of-Care Detection of COVID-19

Navchetan Awasthi

Abstract:

Lung ultrasound (US) imaging has the potential to be an effective point-of-care test for detection of
COVID-19, due to its ease of operation with minimal personal
protection equipment along with easy disinfection. The current state-of-the-art deep learning models for detection of
COVID-19 are heavy models that may not be easy to deploy
in commonly utilized mobile platforms in point-of-care testing. In this work, we develop a lightweight mobile friendly
efficient deep learning model for detection of COVID-19
using lung US images. Three different classes including
COVID-19, pneumonia, and healthy were included in this
task. The developed network, named as Mini-COVIDNet, was
bench-marked with other light weight neural network models
along with state-of-the-art heavy model. It was shown that
the proposed network can achieve the highest accuracy
of 83.2% and requires a training time of only 24 min. The
proposed Mini-COVIDNet has 4.39 times less number of
parameters in the network compared to its next best performing network and requires a memory of only 51.29 MB,
making the point-of-care detection of COVID-19 using lung
US imaging plausible on a mobile platform. Deployment of
these lightweight networks on embedded platforms shows
that the proposed Mini-COVIDNet is highly versatile and
provides optimal performance in terms of being accurate as
well as having latency in the same order as other lightweight
networks. The developed lightweight models are available at
https://github.com/navchetan-awasthi/Mini-COVIDNet.