A robust ensemble-deep learning model for COVID-19 diagnosis based on an integrated CT scan images database

Maede Maftouni, Andrew Chung Chee Law, Bo Shen, Zhenyu Kong Grado, Yangze Zhou, Niloofar Ayoobi Yazdi

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

43 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationIISE Annual Conference and Expo 2021
EditorsA. Ghate, K. Krishnaiyer, K. Paynabar
PublisherInstitute of Industrial and Systems Engineers, IISE
Pages632-637
Number of pages6
ISBN (Electronic)9781713838470
StatePublished - 2021
Externally publishedYes
EventIISE Annual Conference and Expo 2021 - Virtual, Online
Duration: May 22 2021May 25 2021

Publication series

NameIISE Annual Conference and Expo 2021

Conference

ConferenceIISE Annual Conference and Expo 2021
CityVirtual, Online
Period5/22/215/25/21

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Keywords

  • Attention mechanism
  • COVID-19 diagnosis
  • Chest CT scan dataset
  • Convolutional neural network
  • Deep learning

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