TY - GEN
T1 - A Highly Secure and Accurate System for COVID-19 Diagnosis from Chest X-Ray Images
AU - Nguyen, Tuy Tan
AU - Chen, Tianyi
AU - Philippi, Ian
AU - Phan, Quoc Bao
AU - Kudo, Shunri
AU - Huda, Samsul
AU - Nogami, Yasuyuki
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Computer-aid diagnosis
KW - COVID-19
KW - image classification
KW - Kyber
UR - http://www.scopus.com/inward/record.url?scp=85204959183&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85204959183&partnerID=8YFLogxK
U2 - 10.1109/MWSCAS60917.2024.10658795
DO - 10.1109/MWSCAS60917.2024.10658795
M3 - Conference contribution
AN - SCOPUS:85204959183
T3 - Midwest Symposium on Circuits and Systems
SP - 980
EP - 984
BT - 2024 IEEE 67th International Midwest Symposium on Circuits and Systems, MWSCAS 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 67th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2024
Y2 - 11 August 2024 through 14 August 2024
ER -