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

2021-05-20 17:14:01
Shehan Perera, Srikar Adhikari, Alper Yilmaz
arXiv_CV
         
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.

Abstract (translated)

URL

https://arxiv.org/abs/2105.09913

PDF

https://arxiv.org/pdf/2105.09913.pdf

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