TY - GEN
T1 - A robust ensemble-deep learning model for COVID-19 diagnosis based on an integrated CT scan images database
AU - Maftouni, Maede
AU - Law, Andrew Chung Chee
AU - Shen, Bo
AU - Kong Grado, Zhenyu
AU - Zhou, Yangze
AU - Yazdi, Niloofar Ayoobi
N1 - Publisher Copyright:
© 2021 IISE Annual Conference and Expo 2021. All rights reserved.
PY - 2021
Y1 - 2021
N2 - The use of efficient computer-aided medical diagnosis has never been more critical, given the global extent of COVID-19 and the consequent depletion of hospital resources. Artificial intelligence (AI) powered COVID-19 detection can facilitate an early diagnosis of this highly contagious disease and further reduce the infectivity and mortality rates. The preferred imaging option for COVID-19 diagnosis is computed tomography (CT). However, there is an inevitable inter- and intra-observer variability when using CT scans for diagnosis leading to label noise. Label noise can significantly affect the performance of deep learning models. We present a robust COVID-19 classifier on chest CT scan images with noisy labels by proposing an ensemble deep learning model of pretrained Residual Attention and DenseNet architectures. The novelty of this method is that these two deep networks consolidate each other by focusing on complementary, attention-aware, and global sets of features. Specifically, the features extracted from the two deep networks (base-learners) are stacked together and processed by a meta-learner to provide the final robust prediction. Additionally, we have built a large and nationally diverse COVID-19 CT scan dataset by curating open-source datasets to improve the generalizability of our classifier. Extensive experimental evaluations on our dataset illustrate the improved performance of our ensemble model over the base-learners.
AB - The use of efficient computer-aided medical diagnosis has never been more critical, given the global extent of COVID-19 and the consequent depletion of hospital resources. Artificial intelligence (AI) powered COVID-19 detection can facilitate an early diagnosis of this highly contagious disease and further reduce the infectivity and mortality rates. The preferred imaging option for COVID-19 diagnosis is computed tomography (CT). However, there is an inevitable inter- and intra-observer variability when using CT scans for diagnosis leading to label noise. Label noise can significantly affect the performance of deep learning models. We present a robust COVID-19 classifier on chest CT scan images with noisy labels by proposing an ensemble deep learning model of pretrained Residual Attention and DenseNet architectures. The novelty of this method is that these two deep networks consolidate each other by focusing on complementary, attention-aware, and global sets of features. Specifically, the features extracted from the two deep networks (base-learners) are stacked together and processed by a meta-learner to provide the final robust prediction. Additionally, we have built a large and nationally diverse COVID-19 CT scan dataset by curating open-source datasets to improve the generalizability of our classifier. Extensive experimental evaluations on our dataset illustrate the improved performance of our ensemble model over the base-learners.
KW - Attention mechanism
KW - COVID-19 diagnosis
KW - Chest CT scan dataset
KW - Convolutional neural network
KW - Deep learning
UR - http://www.scopus.com/inward/record.url?scp=85120875002&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85120875002&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85120875002
T3 - IISE Annual Conference and Expo 2021
SP - 632
EP - 637
BT - IISE Annual Conference and Expo 2021
A2 - Ghate, A.
A2 - Krishnaiyer, K.
A2 - Paynabar, K.
PB - Institute of Industrial and Systems Engineers, IISE
T2 - IISE Annual Conference and Expo 2021
Y2 - 22 May 2021 through 25 May 2021
ER -