Generation of Novel Synthetic Portable Chest X-Ray Images for Automatic COVID-19 Screening
Title
Generation of Novel Synthetic Portable Chest X-Ray Images for Automatic COVID-19 ScreeningDate
2022Citation
D. Iglesias Morís, J. de Moura, J. Novo, and M. Ortega, "Generation of Novel Synthetic Portable Chest X-Ray Images for Automatic COVID-19 Screening", in: Rajae El Ouazzani, Mohammed Fattah, Nabil Benamar (eds.) AI Applications for Disease Diagnosis and Treatment, IGI Global, 2022, doi: https://doi.org/10.4018/978-1-6684-2304-2.ch008
Abstract
[Abstract]: The diagnosis and the study of the evolution of COVID-19 is crucial to tackle the challenge that this disease represents for healthcare services. Chest x-ray imaging allows us to visualize the pulmonary regions, where COVID-19 causes its main affectation. In order to reduce the risk of cross-contamination, a crucial aspect in the pandemic, portable chest x-ray devices are advantageous being easier to decontaminate in comparison with the fixed machinery, despite offering a lower image quality. Furthermore, the recent emergence of COVID-19 implies a data scarcity that must be tackled. In this chapter, the authors present the analysis of a strategy that generates novel synthetic portable chest x-ray images using the CycleGAN, an architecture for image translation that is trained with unpaired data. The novel set of images is then added to the original dataset, improving the performance of the classification model.
Keywords
Computer-Aided Diagnosis
Deep Learning
Biomedical Imaging
Image Generation
CycleGAN
Image Translation
Computer Vision
Classification
Deep Learning
Biomedical Imaging
Image Generation
CycleGAN
Image Translation
Computer Vision
Classification
Description
in: Rajae El Ouazzani, Mohammed Fattah, Nabil Benamar (eds.) AI Applications for Disease Diagnosis and Treatment, IGI Global, 2022
Editor version
Rights
©2022 IGI Global.