2022-06-11 21:17:42
Harold Brayan Arteaga-Arteaga (1), Melissa delaPava (1), Alejandro Mora-Rubio (1), Mario Alejandro Bravo-Ortíz (1), Jesus Alejandro Alzate-Grisales (1), Daniel Arias-Garzón (1), Luis Humberto López-Murillo (2), Felipe Buitrago-Carmona (3), Juan Pablo Villa-Pulgarín (1), Esteban Mercado-Ruiz (1), Simon Orozco-Arias (3 and 4), M. Hassaballah (5), Maria de la Iglesia-Vaya (6), Oscar Cardona-Morales (1), Reinel Tabares-Soto (1) ((1) Department of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Colombia, (2) Department of Chemical Engineering, Universidad Nacional de Colombia, Manizales, Colombia, (3) Department of Computer Science, Universidad Autónoma de Manizales, Manizales, Colombia, (4) Department of Systems and informatics, Universidad de Caldas, Manizales, Colombia, (5) Faculty of Computers and Information, South Valley University, Qena, Egypt, (6) Unidad Mixta de Imagen Biomédica FISABIO-CIPF, Fundación para el Fomento de la Investigación Sanitario y Biomédica de la Comunidad Valenciana, Valencia, Spain)
Abstract
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.
Abstract (translated)
URL
https://arxiv.org/abs/2206.05615
PDF
https://arxiv.org/pdf/2206.05615.pdf
