Mostrar el registro sencillo del ítem
Segmentation of Lung Lesions Caused by COVID-19 in Computed Tomography Images Using Deep Learning
dc.contributor.author | Díaz Román, José David | |
dc.date.accessioned | 2023-12-28T18:40:33Z | |
dc.date.available | 2023-12-28T18:40:33Z | |
dc.date.issued | 2023-08-22 | es_MX |
dc.identifier.isbn | 978-3-031-34600-2 | es_MX |
dc.identifier.uri | http://cathi.uacj.mx/20.500.11961/26604 | |
dc.description.abstract | Smart cities are transforming the way people live, work, and socialize. Smart cities offer a wide range of opportunities to improve the quality of life for citizens, and smart healthcare is one of the most promising areas. In this regard, smart cities can play a key role in providing a robust infrastructure and data platform for healthcare innovation. For example, the implementation of sensor technologies and data analytics in smart cities can enable healthcare professionals to gather valuable information about population health, identify disease patterns and trends, and act preventively to address public health challenges. In addition, smart cities can integrate telemedicine technologies to provide remote, real-time medical care, which can be especially useful in remote or hard-to-reach areas. Furthermore, the internet of things and big data technologies can be merged with smart health to address some of the issues with increasing the accessibility, affordability, and availability of healthcare. The connection between smart cities and smart healthcare has the potential to significantly improve healthcare and the well-being of the population | es_MX |
dc.description.uri | https://link.springer.com/chapter/10.1007/978-3-031-34601-9_14 | es_MX |
dc.language.iso | en | es_MX |
dc.publisher | Springer | es_MX |
dc.relation.ispartof | Producto de investigación IIT | es_MX |
dc.relation.ispartof | Instituto de Ingeniería y Tecnología | es_MX |
dc.rights | Atribución-NoComercial-SinDerivadas 2.5 México | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/2.5/mx/ | * |
dc.subject | COVID-19 | es_MX |
dc.subject | Lung lesion segmentation | es_MX |
dc.subject | Computed tomography | es_MX |
dc.subject | Deep learning | es_MX |
dc.subject.other | info:eu-repo/classification/cti/7 | es_MX |
dc.title | Segmentation of Lung Lesions Caused by COVID-19 in Computed Tomography Images Using Deep Learning | es_MX |
dc.type | Capítulo de libro | es_MX |
dcterms.thumbnail | http://ri.uacj.mx/vufind/thumbnails/rupiiit.png | es_MX |
dcrupi.instituto | Instituto de Ingeniería y Tecnología | es_MX |
dcrupi.cosechable | Si | es_MX |
dcrupi.subtipo | Investigación | es_MX |
dcrupi.nopagina | 237-259 | es_MX |
dcrupi.alcance | Internacional | es_MX |
dcrupi.pais | Suiza | es_MX |
dc.identifier.doi | https://doi.org/10.1007/978-3-031-34601-9 | es_MX |
dc.contributor.coauthor | Mederos, Boris | |
dc.contributor.coauthor | Cota Ruiz, Juan De Dios | |
dc.contributor.coauthor | Enriquez Aguilera, Francisco Javier | |
dc.contributor.alumno | 168463 | es_MX |
dcrupi.titulolibro | Internet of Everything for Smart City and Smart Healthcare Applications | es_MX |
dcrupi.impactosocial | No | es_MX |
dcrupi.vinculadoproyext | No | es_MX |
dcrupi.pronaces | Salud | es_MX |
dcrupi.vinculadoproyint | No | es_MX |
Archivos en el ítem
Este ítem aparece en la(s) siguiente(s) colección(ones)
-
Capítulo en libro [232]