A TinyML based Portable, Low-Cost Microwave Head Imaging System for Brain Stroke Detection

Muhammad Hashir*, Nazish Khalid, Nasir Mahmood, Muhammad A. Rehman, Muhammad Asad, Muhammad Q. Mehmood, Muhammad Zubair, Yehia Massoud*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Microwave Imaging (MWI) has emerged as a potential candidate for brain stroke detection due to its low cost, time efficiency and accurate nature when compared to other screening techniques. TinyML is a revolutionary technique for utilizing AI in portable and low-powered devices. The need for more compact and concise systems grows by the day in order to provide smart services, particularly in the medical arena. This paper tries to fulfil these requirements by presenting the first-ever portable MWI-based TinyML brain stroke detection system with high accuracy. The head-imaging dataset, utilized here for the training of models, provides open-source data generated by our prototype head imaging system consisting of a low-cost vector network analyzer, single-board computer, rotating motor setup, and a Vivaldi antenna. The Tiny ML model is a compressed-size model of our proposed Deep Learning (DL) framework that obtains an accuracy of 93% on testing data with an F1-score of 0.929 deployed on the single-board computer. The compressed model obtained by pruning or quantization is not only small in size but also retains the above 90% accuracy of the DL model. This work reassures the possibility of successful deployment of Tiny ML- based solutions in microwave imaging systems for medical diagnostic applications in low-resource settings.

Original languageEnglish (US)
Title of host publicationISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665451093
DOIs
StatePublished - 2023
Event56th IEEE International Symposium on Circuits and Systems, ISCAS 2023 - Monterey, United States
Duration: May 21 2023May 25 2023

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2023-May
ISSN (Print)0271-4310

Conference

Conference56th IEEE International Symposium on Circuits and Systems, ISCAS 2023
Country/TerritoryUnited States
CityMonterey
Period05/21/2305/25/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Brain Stroke
  • Deep Learning (DL)
  • Machine Learning (ML)
  • Microwave Imaging(MWI)
  • TinyML

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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