Cov-caldas: A new COVID-19 chest X-Ray dataset from state of Caldas-Colombia
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...
- Autores:
-
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, ario Alfonso
Jaramillo-Robled, Oscar
de la Iglesia-Vayá, Maria
Tabares-Soto, Reinel
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12339
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12339
- Palabra clave:
- Object Detection;
Deep Learning;
IOU
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
| Summary: | 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. © 2022, The Author(s). |
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