TY - GEN
T1 - COVIDDX
T2 - 14th International Conference on Health Informatics, HEALTHINF 2021 - Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021
AU - Mayya, Veena
AU - Karthik, K.
AU - Kamath, Sowmya S.
AU - Karadka, Krishnananda
AU - Jeganathan, Jayakumar
N1 - Publisher Copyright:
Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
PY - 2021
Y1 - 2021
N2 - The COVID-19 pandemic has affected the world on a global scale, infecting nearly 68 million people across the world, with over 1.5 million fatalities as of December 2020. A cost-effective early-screening strategy is crucial to prevent new outbreaks and to curtail the rapid spread. Chest X-ray images have been widely used to diagnose various lung conditions such as pneumonia, emphysema, broken ribs and cancer. In this work, we explore the utility of chest X-ray images and available expert-written diagnosis reports, for training neural network models to learn disease representations for diagnosis of COVID-19. A manually curated dataset consisting of 450 chest X-rays of COVID-19 patients and 2,000 non-COVID cases, along with their diagnosis reports were collected from reputed online sources. Convolutional neural network models were trained on this multimodal dataset, for prediction of COVID-19 induced pneumonia. A comprehensive clinical decision support system powered by ensemble deep learning models (CADNN) is designed and deployed on the web. The system also provides a relevance feedback mechanism through which it learns multimodal COVID-19 representations for supporting clinical decisions.
AB - The COVID-19 pandemic has affected the world on a global scale, infecting nearly 68 million people across the world, with over 1.5 million fatalities as of December 2020. A cost-effective early-screening strategy is crucial to prevent new outbreaks and to curtail the rapid spread. Chest X-ray images have been widely used to diagnose various lung conditions such as pneumonia, emphysema, broken ribs and cancer. In this work, we explore the utility of chest X-ray images and available expert-written diagnosis reports, for training neural network models to learn disease representations for diagnosis of COVID-19. A manually curated dataset consisting of 450 chest X-rays of COVID-19 patients and 2,000 non-COVID cases, along with their diagnosis reports were collected from reputed online sources. Convolutional neural network models were trained on this multimodal dataset, for prediction of COVID-19 induced pneumonia. A comprehensive clinical decision support system powered by ensemble deep learning models (CADNN) is designed and deployed on the web. The system also provides a relevance feedback mechanism through which it learns multimodal COVID-19 representations for supporting clinical decisions.
UR - http://www.scopus.com/inward/record.url?scp=85103831752&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85103831752&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85103831752
T3 - HEALTHINF 2021 - 14th International Conference on Health Informatics; Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021
SP - 659
EP - 666
BT - HEALTHINF 2021 - 14th International Conference on Health Informatics; Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021
A2 - Pesquita, Catia
A2 - Fred, Ana
A2 - Gamboa, Hugo
PB - SciTePress
Y2 - 11 February 2021 through 13 February 2021
ER -
