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

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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
Early online date22 Dec 2020
Publication statusE-pub ahead of print - 22 Dec 2020


  • Generative adversarial networks
  • Deep neural network
  • covid-19
  • medical informatics
  • COVID-19
  • Deep neural networks
  • Bacterial pneumonia
  • Chest x-rays
  • Medical informatics

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