Lightweight deep learning models for detecting COVID-19 from chest X-ray images

Stefanos Karakanis, Georgios Leontidis* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
21 Downloads (Pure)

Abstract

Deep learning methods have already enjoyed an unprecedented success in medical imaging problems. Similar success has been evidenced when it comes to the detection of COVID-19 from medical images, therefore deep learning approaches are considered good candidates for detecting this disease, in collaboration with radiologists and/or physicians. In this paper, we propose a new approach to detect COVID-19 via exploiting a conditional generative adversarial network to generate synthetic images for augmenting the limited amount of data available. Additionally, we propose two deep learning models following a lightweight architecture, commensurating with the overall amount of data available. Our experiments focused on both binary classification for COVID-19 vs Normal cases and multi-classification that includes a third class for bacterial pneumonia. Our models achieved a competitive performance compared to other studies in literature and also a ResNet8 model. Our binary model achieved 98.7% accuracy, 100% sensitivity and 98.3% specificity, while our three-class model achieved 98.3% accuracy, 99.3% sensitivity and 98.1% specificity. Moreover, via adopting a testing protocol proposed in literature, our models proved to be more robust and reliable in COVID-19 detection than a baseline ResNet8, making them good candidates for detecting COVID-19 from posteroanterior chest X-ray images.
Original languageEnglish
Article number104181
Number of pages9
JournalComputers in Biology and Medicine
Volume130
Early online date22 Dec 2020
DOIs
Publication statusPublished - 1 Mar 2021

Keywords

  • Generative adversarial networks
  • Deep neural network
  • covid-19
  • medical informatics
  • COVID-19
  • Deep neural networks
  • Bacterial pneumonia
  • Chest x-rays
  • Medical informatics
  • Models, Theoretical
  • Lung/diagnostic imaging
  • Humans
  • Male
  • Tomography, X-Ray Computed
  • Deep Learning
  • SARS-CoV-2
  • COVID-19/diagnostic imaging
  • Female

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