Deep Learning Applied to COVID-19 Detection in X-Ray Images

Abstract

COVID-19 caused by the SARS-CoV-2 virus has affected healthcare and people's lifestyles worldwide since 2019. Among the available diagnostic tools, reverse transcription-polymerase chain reaction has proven highly accurate. However, the need for a specialized laboratory makes these tests expensive and time-consuming between sample collection and results. Currently, there are initial steps for the diagnosis of COVID-19 through chest x-ray images. Additionally, artificial intelligence techniques like deep learning (DL) help identify abnormalities. Inspired by the reported success of DL, this chapter presents an introduction to state-of-the-art DL-based approaches applied to the detection of COVID-19 in chest x-ray images, which currently allows assessing disease severity. The results presented are obtained using well-known models and some novel networks designed for this task. In addition, the models were evaluated using the most used public datasets, applying preprocessing techniques to improve detection results. Finally, this chapter shows some possible future research directions.