POCFORMER: A LIGHTWEIGHT TRANSFORMER ARCHITECTURE FOR DETECTION OF COVID-19 USING POINT OF CARE ULTRASOUND

ABSTRACT

The rapid and seemingly endless expansion of COVID-19 can be traced back to the inefficiency and shortage of testing kits that offer accurate results in a timely manner. An emerging popular technique, which adopts improvements made in mobile ultrasound technology, allows for healthcare professionals to conduct rapid screenings on a large scale. We present an image-based solution that aims at automating the testing process which allows for rapid mass testing to be conducted with or without a trained medical professional that can be applied to rural environment and third world countries. Our contributions towards rapid large-scale testing includes a novel deep learning architecture capable of analyzing ultrasound data that can run in real time and significantly improve the current state-of-the-art detection accuracies using image based COVID-19 detection. © 2021 IEEE.