Deep Learning for Diagonal Earlobe Crease Detection

Sara L. Almonacid-Uribe, Oliverio J. Santana, D. Hernández-Sosa, David Freire-Obregón
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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.
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对角耳垂折痕检测的深度学习
2022年6月发表在《今日医学新闻》上的一篇文章在标题中提出了一个基本问题:耳垂褶皱能预测心脏病发作吗?作者解释说,末梢动脉供应心脏和耳朵。换句话说,如果它们失去血液供应,没有其他动脉可以接管,导致组织损伤。因此,一些耳垂有对角线折痕,线,或深褶皱,类似于皱纹。在本文中,我们朝着检测这种特定标记迈出了一步,通常称为DELC或弗兰克标志。出于这个原因,我们向公众提供了第一个DELC数据集。此外,我们还研究了许多尖端骨干在注释照片上的性能。通过实验,我们证明可以通过将预训练的编码器与定制的分类器相结合来解决这一挑战,达到97.7%的准确率。此外,我们分析了性能和大小之间的主干权衡,估计MobileNet是最有前途的编码器。
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