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

Shehan Perera, Srikar Adhikari, Alper Yilmaz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
PublisherIEEE Computer Society
Pages195-199
Number of pages5
ISBN (Electronic)9781665441155
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE International Conference on Image Processing, ICIP 2021 - Anchorage, United States
Duration: Sep 19 2021Sep 22 2021

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2021-September
ISSN (Print)1522-4880

Conference

Conference2021 IEEE International Conference on Image Processing, ICIP 2021
Country/TerritoryUnited States
CityAnchorage
Period9/19/219/22/21

Keywords

  • Covid-19 diagnosis
  • Deep learning
  • Transformer networks
  • Ultrasound

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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