A Highly Secure and Accurate System for COVID-19 Diagnosis from Chest X-Ray Images

Tuy Tan Nguyen, Tianyi Chen, Ian Philippi, Quoc Bao Phan, Shunri Kudo, Samsul Huda, Yasuyuki Nogami

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

Abstract

Global healthcare systems face growing pressure as populations rise. This can lead to longer wait times and an increased risk of treatment delays or misdiagnosis. Artificial intelligence (AI) diagnostic systems are being developed to address these challenges, but concerns exist about their accuracy and data security. This study introduces a robust AI telehealth system that offers a two-pronged approach. It utilizes a cutting-edge image analysis method, vision transformer, to enhance diagnostic accuracy, while also incorporating post-quantum cryptography algorithm, Kyber, to ensure patient privacy. Furthermore, an interactive visualization tool aids in interpreting the diagnostic results, providing valuable insights into the model's decisionmaking process. This translates to faster diagnoses and potentially shorter wait times for patients. Extensive testing with various datasets has demonstrated the system's effectiveness. The optimized model achieves a remarkable 95.79% accuracy rate in diagnosing COVID-19 from chest X-rays, with the entire process completed in under five seconds.

Original languageEnglish (US)
Title of host publication2024 IEEE 67th International Midwest Symposium on Circuits and Systems, MWSCAS 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages980-984
Number of pages5
ISBN (Electronic)9798350387179
DOIs
StatePublished - 2024
Externally publishedYes
Event67th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2024 - Springfield, United States
Duration: Aug 11 2024Aug 14 2024

Publication series

NameMidwest Symposium on Circuits and Systems
ISSN (Print)1548-3746

Conference

Conference67th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2024
Country/TerritoryUnited States
CitySpringfield
Period8/11/248/14/24

Keywords

  • Computer-aid diagnosis
  • COVID-19
  • image classification
  • Kyber

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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