Neural Style Transfer as Data Augmentation for Improving COVID-19 Diagnosis Classification

Netzahualcoyotl Hernandez-Cruz, David Cato, Jesus Favela

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
69 Downloads (Pure)


Coronavirus disease 2019 (COVID-19) has accounted for millions of causalities. While it affects not only individuals but also our collective healthcare and economic systems, testing is insufficient and costly hampering efforts to deal with the pandemic. Chest X-rays are routine radiographic imaging tests that are used for the diagnosis of respiratory conditions such as pneumonia and COVID-19. Convolutional neural networks have shown promise to be effective at classifying X-rays for assisting diagnosis of conditions; however, achieving robust performance demanded in most modern medical applications typically requires a large number of samples. While there exist datasets containing thousands of X-ray images of patients with healthy and pneumonia diagnoses, because COVID-19 is such a recent phenomenon, there are relatively few confirmed COVID-19 positive chest X-rays openly available to the research community. In this paper, we demonstrate the effectiveness of cycle-generative adversarial network, commonly used for neural style transfer, as a way to augment COVID-19 negative X-ray images to look like COVID-19 positive images for increasing the number of COVID-19 positive training samples. The statistical results show an increase in the mean macro f1-score over 21% on a one-tailed t score = 2.68 and p value = 0.01 to accept our alternative hypothesis for an α = 0.05 . We conclude that this approach, when used in conjunction with standard transfer learning techniques, is effective at improving the performance of COVID-19 classifiers for a variety of common convolutional neural networks.

Original languageEnglish
Article number410
Number of pages12
JournalSN Computer Science
Issue number5
Early online date13 Aug 2021
Publication statusPublished online - 13 Aug 2021

Bibliographical note

© The Author(s) 2021.


  • generative adversarial networks
  • Transfer learning
  • convolutional neural network
  • Neural style transfer
  • data augmentation


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