POCFormer: A Lightweight Transformer Architecture for Detection of COVID-19 Using Point of Care Ultrasound

TL;DR

POCFormer introduces a lightweight transformer model designed for real-time analysis of point-of-care ultrasound images, enabling rapid and accurate COVID-19 detection suitable for large-scale screening in resource-limited settings.

Contribution

It proposes a novel deep learning architecture that enhances COVID-19 detection accuracy from ultrasound images and operates efficiently in real-time.

Findings

Improved detection accuracy over existing methods

Real-time analysis capability

Suitable for large-scale deployment in rural and resource-limited environments

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 environments and third world countries. Our contributions towards rapid large-scale testing include 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.