System for quantitative diagnosis of COVID-19 associated Pneumonia based on Superpixels with deep learning and chest CT


  • Everton Castelão Tetila
  • Keno Kyrill Bressem
  • Gilberto Astolfi
  • Diego André Sant’Ana
  • Marcio Carneiro Brito Pache
  • Gelson Wirti Junior
  • Jayme Garcia Arnal Barbedo
  • Hemerson Pistori



lung infection, computer vision system, respiratory syndrome, Coronavirus, machine learning


COVID-19 is a disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that can lead to complications such as acute respiratory distress syndrome, acute heart injury and secondary infections in a relatively high proportion of patients and, consequently, lead to significant mortality. The definitive diagnosis of COVID-19 is performed by real-time Polymerase Chain Reaction (RT-PCR). However, as RT-PCR results usually take longer to be completed than computed tomography (CT), the latter has had an important role in the detection of patients infected with COVID-19. A rough estimate of the extent of lung involvement by the disease is also important and considered an additional criterion for deciding on discharge or hospitalization. Recent research has adopted deep neural networks and other machine learning approaches to detect the presence of lung infection caused by COVID-19. However, the extent of lung involvement (volume) caused by the disease has been only superficially investigated. In this work, we created an end-to-end computer vision system to automatically quantify the Percentage of Infection (POI) in chest CT images of COVID-19 cases confirmed by laboratorial analysis. We evaluated the performance of three well-known deep neural networks, trained using three different strategies: (1) no transfer learning with randomly initialized weights; (2) transfer learning using with ImageNet weights without fine-tuning; and (3) transfer learning with weight fine-tuning. Data augmentation and dropout were used during training to reduce overfitting and increase the generalization capacity of the models. Our approach consists of segmenting a chest CT with the SLIC Superpixels method and classifying each segment (superpixel) into a specific class (COVID or non-COVID). We used the weights of the best deep neural network to classify the superpixels and quantify the COVID-19 infection by calculating the POI on chest CT. The results indicate that deep learning models can be successfully used to support radiologists in the quantitative diagnosis of lung infection caused by COVID-19, reaching an accuracy of up to 98.4% with Inception-Resnet-v2 architecture.


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How to Cite

Tetila, E. C., Bressem, K. K., Astolfi, G., Sant’Ana, D. A., Pache, M. C. B., Wirti Junior, G., Barbedo, J. G. A., & Pistori, H. (2023). System for quantitative diagnosis of COVID-19 associated Pneumonia based on Superpixels with deep learning and chest CT. OBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA, 21(9), 10883–10905.




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