Please use this identifier to cite or link to this item:
http://hdl.handle.net/10553/124081
Title: | Deep learning for diagonal earlobe crease detection | Authors: | Almonacid-Uribe, Sara L. Santana Jaria, Oliverio Jesús Hernández-Sosa, Daniel Freire-Obregón, David |
UNESCO Clasification: | 1203 Ciencia de los ordenadores 220990 Tratamiento digital. Imágenes |
Keywords: | Computer vision Diagonal earlobe crease DELC Frank’s sign Cardiovascular disease, et al |
Issue Date: | 2023 | Publisher: | SciTePress Digital Library | Project: | 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 |
Journal: | International Conference On Pattern Recognition Applications And Methods | Conference: | 12th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2023) | Abstract: | 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 | Source: | In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM - V. 1, p. 74-81, 2023 , Lisbon, Portugal |
Appears in Collections: | Actas de congresos |
SCOPUSTM
Citations
1
checked on Nov 17, 2024
Page view(s)
29
checked on Oct 14, 2023
Google ScholarTM
Check
Altmetric
Share
Export metadata
Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.