Implementing a Detection System for COVID-19 based on Lung Ultrasound Imaging and Deep Learning
This addresses the need for fast, automated diagnostic tools during the COVID-19 pandemic, particularly in resource-limited settings, but appears incremental as it applies existing deep learning methods to a new medical imaging modality.
The paper tackles the problem of COVID-19 detection by developing a system based on lung ultrasound imaging and deep learning, implemented on a Raspberry Pi for portability and use in remote areas without internet.
The COVID-19 pandemic started in China in December 2019 and quickly spread to several countries. The consequences of this pandemic are incalculable, causing the death of millions of people and damaging the global economy. To achieve large-scale control of this pandemic, fast tools for detection and treatment of patients are needed. Thus, the demand for alternative tools for the diagnosis of COVID-19 has increased dramatically since accurated and automated tools are not available. In this paper we present the ongoing work on a system for COVID-19 detection using ultrasound imaging and using Deep Learning techniques. Furthermore, such a system is implemented on a Raspberry Pi to make it portable and easy to use in remote regions without an Internet connection.