Data augmentation approaches using cycle-consistent adversarial networks for improving COVID-19 screening in portable chest X-ray images
作者：
Highlights：
• The computer-aided diagnosis of COVID-19 in chest X-ray images helps specialists.
• A COVID-19 screening improval is proposed, considering a powerful data augmentation.
• Data augmentation generates synthetic images using the 3 CycleGAN architectures.
• Portable devices help to prevent contagion, and are the recommended during pandemic.
• Only fully automatic methodology designed to work with portable X-ray devices.
摘要
•The computer-aided diagnosis of COVID-19 in chest X-ray images helps specialists.•A COVID-19 screening improval is proposed, considering a powerful data augmentation.•Data augmentation generates synthetic images using the 3 CycleGAN architectures.•Portable devices help to prevent contagion, and are the recommended during pandemic.•Only fully automatic methodology designed to work with portable X-ray devices.
论文关键词：X-ray portable device,COVID-19,Data augmentation,Screening,CycleGAN,Deep learning
论文评审过程：Received 15 April 2021, Revised 2 July 2021, Accepted 25 July 2021, Available online 31 July 2021, Version of Record 4 August 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115681
