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ULNet for the detection of coronavirus (COVID-19) from chest X-ray images
Elsevier Ltd
  • Tianbo Wu, Tianjin University
  • Chen Tang, Tianjin University
  • Min Xu, Tianjin University
  • Nian Hong, Tianjin University
  • Zhenkun Lei, Dalian University of Technology
Document Type

Novel coronavirus disease 2019 (COVID-19) is an infectious disease that spreads very rapidly and threatens the health of billions of people worldwide. With the number of cases increasing rapidly, most countries are facing the problem of a shortage of testing kits and resources, and it is necessary to use other diagnostic methods as an alternative to these test kits. In this paper, we propose a convolutional neural network (CNN) model (ULNet) to detect COVID-19 using chest X-ray images. The proposed architecture is constructed by adding a new downsampling side, skip connections and fully connected layers on the basis of U-net. Because the shape of the network is similar to UL, it is named ULNet. This model is trained and tested on a publicly available Kaggle dataset (consisting of a combination of 219 COVID-19, 1314 normal and 1345 viral pneumonia chest X-ray images), including binary classification (COVID-19 vs. Normal) and multiclass classification (COVID-19 vs. Normal vs. Viral Pneumonia). The accuracy of the proposed model in the detection of COVID-19 in the binary-class and multiclass tasks is 99.53% and 95.35%, respectively. Based on these promising results, this method is expected to help doctors diagnose and detect COVID-19. Overall, our ULNet provides a quick method for identifying patients with COVID-19, which is conducive to the control of the COVID-19 pandemic. © 2021 Elsevier Ltd

Publication Date
  • Classification (of information); Convolutional neural networks; Deep learning; Diagnosis; Image classification; Chest X-ray image; Convolutional neural network; Coronavirus disease 2019; Coronaviruses; Deep learning; Diagnostic methods; Infectious disease; Neural network model; Test kits; ULNet; Coronavirus; Article; artificial neural network; bacterial pneumonia; binary classification; computer assisted tomography; controlled study; convolutional neural network; coronavirus disease 2019; deconvolution; deep learning; diagnostic accuracy; human; image segmentation; major clinical study; multiclass classification; nonlinear system; support vector machine; thorax radiography; transfer of learning; virus pneumonia; pandemic; X ray; COVID-19; Humans; Pandemics; SARS-CoV-2; X-Rays

IR Deposit conditions:

  • OA version (pathway b)
  • Accepted version 12 month embargo
  • Must link to publisher version with DOI
Citation Information
T. Wu, C. Tang, M. Xu, N. Hong, and Z. Lei, "ULNet for the detection of coronavirus (COVID-19) from chest X-ray images", Computers in Biology and Medicine, vol. 137, no. 104834, Oct. 2021, 10.1016/j.compbiomed.2021.104834