Deep Learning for Diagonal Earlobe Crease Detection

TL;DR

This paper introduces a new dataset and deep learning approach for detecting diagonal earlobe creases, a potential marker for heart attack risk, achieving high accuracy with pre-trained models.

Contribution

The paper provides the first publicly available DELC dataset and evaluates various deep learning backbones for automatic detection of earlobe creases.

Findings

Achieved 97.7% accuracy in DELC detection.

MobileNet identified as the best trade-off between performance and size.

Demonstrated the feasibility of automated DELC detection using deep learning.

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.