Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/129689
Title: Exploring Data Augmentation Strategies for Diagonal Earlobe Crease Detection
Authors: Almonacid-Uribe, Sara
Santana, Oliverio J. 
Hernández-Sosa, Daniel 
Freire-Obregón, David 
UNESCO Clasification: 220990 Tratamiento digital. Imágenes
Keywords: Cardiovascular Disease
Computer Vision
Coronary Artery Disease
Deep Learning
Delc, et al
Issue Date: 2024
Project: La viabilidad jurídica del documento de voluntades anticipadas
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 
Infraestructura de Computación Científica Para Aplicaciones de Inteligencia Artificialy Simulación Numérica en Medioambientey Gestión de Energías Renovables (Iusiani-Ods) 
Journal: Lecture Notes in Computer Science 
Conference: 12th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2023)
Abstract: Diagonal earlobe crease (DELC), also known as Frank’s sign, is a diagonal crease, line, or deep fold that appears on the earlobe and has been hypothesized to be a potential predictor of heart attacks. The presence of DELC has been linked to cardiovascular disease, atherosclerosis, and increased risk of coronary artery disease. Some researchers believe that DELC may be an indicator of an impaired blood supply to the earlobe, which could reflect similar issues in the heart’s blood supply. However, more research is needed to determine whether DELC is a reliable marker for identifying individuals at risk of heart attacks or other cardiovascular problems. To this end, the authors have released the first DELC dataset to the public and investigated the performance of numerous state-of-the-art backbones on annotated photos. The experiments demonstrated that combining pre-trained encoders with a customized classifier achieved 97.7% accuracy, with MobileNet being the most promising encoder in terms of the performance-to-size trade-off.
URI: http://hdl.handle.net/10553/129689
ISBN: 978-3-031-54725-6
ISSN: 0302-9743
DOI: 10.1007/978-3-031-54726-3_1
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [ISSN 0302-9743], v. 14547 LNCS, p. 3-18, (Enero 2024)
Appears in Collections:Actas de congresos
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