In Scientific data
The emergence of COVID-19 as a global pandemic forced researchers worldwide in various disciplines to investigate and propose efficient strategies and/or technologies to prevent COVID-19 from further spreading. One of the main challenges to be overcome is the fast and efficient detection of COVID-19 using deep learning approaches and medical images such as Chest Computed Tomography (CT) and Chest X-ray images. In order to contribute to this challenge, a new dataset was collected in collaboration with "S.E.S Hospital Universitario de Caldas" ( https://hospitaldecaldas.com/ ) from Colombia and organized following the Medical Imaging Data Structure (MIDS) format. The dataset contains 7,307 chest X-ray images divided into 3,077 and 4,230 COVID-19 positive and negative images. Images were subjected to a selection and anonymization process to allow the scientific community to use them freely. Finally, different convolutional neural networks were used to perform technical validation. This dataset contributes to the scientific community by tackling significant limitations regarding data quality and availability for the detection of COVID-19.
Alzate-Grisales Jesús Alejandro, Mora-Rubio Alejandro, Arteaga-Arteaga Harold Brayan, Bravo-Ortiz Mario Alejandro, Arias-Garzón Daniel, López-Murillo Luis Humberto, Mercado-Ruiz Esteban, Villa-Pulgarin Juan Pablo, Cardona-Morales Oscar, Orozco-Arias Simon, Buitrago-Carmona Felipe, Palancares-Sosa Maria Jose, Martínez-Rodríguez Fernanda, Contreras-Ortiz Sonia H, Saborit-Torres Jose Manuel, Montell Serrano Joaquim Ángel, Ramirez-Sánchez María Mónica, Sierra-Gaber Mario Alfonso, Jaramillo-Robledo Oscar, de la Iglesia-Vayá Maria, Tabares-Soto Reinel
2022-Dec-07
