A mask-guided attention deep learning model for COVID-19 diagnosis based on an integrated CT scan images database

Maede Maftouni, Bo Shen, Andrew Chung Chee Law, Niloofar Ayoobi Yazdi, Fahimeh Hadavand, Fereshte Ghiasvand, Zhenyu Kong

Research output: Contribution to journalArticlepeer-review


The global extent of COVID-19 mutations and the consequent depletion of hospital resources highlighted the necessity of effective computer-assisted medical diagnosis. COVID-19 detection mediated by deep learning models can help diagnose this highly contagious disease and lower infectivity and mortality rates. Computed tomography (CT) is the preferred imaging modality for building automatic COVID-19 screening and diagnosis models. It is well-known that the training set size significantly impacts the performance and generalization of deep learning models. However, accessing a large dataset of CT scan images from an emerging disease like COVID-19 is challenging. Therefore, data efficiency becomes a significant factor in choosing a learning model. To this end, we present a multi-task learning approach, namely, a mask-guided attention (MGA) classifier, to improve the generalization and data efficiency of COVID-19 classification on lung CT scan images. The novelty of this method is compensating for the scarcity of data by employing more supervision with lesion masks, increasing the sensitivity of the model to COVID-19 manifestations, and helping both generalization and classification performance. Our proposed model achieves better overall performance than the single-task (without MGA module) baseline and state-of-the-art models, as measured by various popular metrics.

Original languageEnglish (US)
Pages (from-to)132-149
Number of pages18
JournalIISE Transactions on Healthcare Systems Engineering
Issue number2
StatePublished - 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Safety Research
  • Public Health, Environmental and Occupational Health


  • COVID-19 diagnosis
  • Convolutional neural network
  • lung CT scan dataset
  • mask-guided attention
  • model interpretability
  • multi-task learning


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