Paper ID: 2206.05615

Machine learning approaches for COVID-19 detection from chest X-ray imaging: A Systematic Review

Harold Brayan Arteaga-Arteaga, Melissa delaPava, Alejandro Mora-Rubio, Mario Alejandro Bravo-Ortíz, Jesus Alejandro Alzate-Grisales, Daniel Arias-Garzón, Luis Humberto López-Murillo, Felipe Buitrago-Carmona, Juan Pablo Villa-Pulgarín, Esteban Mercado-Ruiz, Simon Orozco-Arias, M. Hassaballah, Maria de la Iglesia-Vaya, Oscar Cardona-Morales, Reinel Tabares-Soto

There is a necessity to develop affordable, and reliable diagnostic tools, which allow containing the COVID-19 spreading. Machine Learning (ML) algorithms have been proposed to design support decision-making systems to assess chest X-ray images, which have proven to be useful to detect and evaluate disease progression. Many research articles are published around this subject, which makes it difficult to identify the best approaches for future work. This paper presents a systematic review of ML applied to COVID-19 detection using chest X-ray images, aiming to offer a baseline for researchers in terms of methods, architectures, databases, and current limitations.

Submitted: Jun 11, 2022