Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/124081
Título: Deep learning for diagonal earlobe crease detection
Autores/as: Almonacid-Uribe, Sara L.
Santana Jaria, Oliverio Jesús 
Hernández-Sosa, Daniel 
Freire-Obregón, David 
Clasificación UNESCO: 1203 Ciencia de los ordenadores
220990 Tratamiento digital. Imágenes
Palabras clave: Computer vision
Diagonal earlobe crease
DELC
Frank’s sign
Cardiovascular disease, et al.
Fecha de publicación: 2023
Editor/a: SciTePress Digital Library 
Proyectos: Interaccióny Re-Identificación de Personas Mediante Machine Learning, Deep Learningy Análisis de Datos Multimodal: Hacia Una Comunicación Más Natural en la Robótica Social 
Re-identificación mUltimodal de participaNtes en competiciones dEpoRtivaS 
Publicación seriada: International Conference On Pattern Recognition Applications And Methods
Conferencia: 12th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2023)
Resumen: An article published on Medical News Today in June 2022 presented a fundamental question in its title: Can an earlobe crease predict heart attacks? The author explained that end arteries supply the heart and ears. In other words, if they lose blood supply, no other arteries can take over, resulting in tissue damage. Consequently, some earlobes have a diagonal crease, line, or deep fold that resembles a wrinkle. In this paper, we take a step toward detecting this specific marker, commonly known as DELC or Frank's Sign. For this reason, we have made the first DELC dataset available to the public. In addition, we have investigated the performance of numerous cutting-edge backbones on annotated photos. Experimentally, we demonstrate that it is possible to solve this challenge by combining pre-trained encoders with a customized classifier to achieve 97.7% accuracy. Moreover, we have analyzed the backbone trade-off between performance and size, estimating MobileNet as the most promising encoder.
URI: http://hdl.handle.net/10553/124081
ISBN: 978-989-758-626-2
ISSN: 2184-4313
DOI: 10.5220/0011644400003411
Fuente: In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM - V. 1, p. 74-81, 2023 , Lisbon, Portugal
Colección:Actas de congresos
Vista completa

Citas SCOPUSTM   

1
actualizado el 21-abr-2024

Visitas

29
actualizado el 14-oct-2023

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.