Deep Learning for Diagonal Earlobe Crease Detection

Sara L. Almonacid-Uribe

, Oliverio J. Santana

Daniel Hern

andez-Sosa

and David Freire-Obreg

SIANI, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

Keywords:

Computer Vision, Diagonal Earlobe Crease, DELC, Frank’s Sign, Cardiovascular Disease, Coronary Artery

Disease, 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.

1 INTRODUCTION

According to the Centers for Disease Control and Pre-

vention (CDC), heart disease is the leading cause of

death for men, women, and the majority of racial

and ethnic groups in the United States (CDC, 2022).

Overall, cardiovascular disease is responsible for one

death every 34 seconds in the United States. Further-

more, one in ﬁve heart attacks are silent; the dam-

age is done, but the individual is unaware (Tsao et al.,

2022). Early detection is essential for providing treat-

ment to alleviate symptoms, reduce mortality, and en-

hance the quality of life (Boudoulas et al., 2016).

As a standard practice, clinicians are taught to di-

agnose coronary artery disease (CAD) based on the

medical history, biomarkers, raw scores, and phys-

ical examinations of individual patients, which they

interpret based on their clinical experience. However,

this approach has evolved due to technological ad-

vances. In the past decade, deep learning (DL) has

demonstrated a promising ability to detect abnormal-

ities in computed tomography (CT) images (Ardila

et al., 2019). Several DL techniques have been pro-

posed to automatically estimate CAD markers from

https://orcid.org/0000-0001-6660-0867

https://orcid.org/0000-0001-7511-5783

https://orcid.org/0000-0003-3022-7698

https://orcid.org/0000-0003-2378-4277

Figure 1: Celebrities exhibiting a DELC marker. In 1987,

the former CNN interviewer Larry King suffered a heart at-

tack and underwent bypass surgery (photo by Eva Rinaldi,

Wikimedia Commons, CC-BY-SA 2.0). In 2009, the for-

mer comedian and actor Robin Williams underwent aortic

valve replacement surgery (photo by Angela George, Wiki-

media Commons, CC-BY 3.0). The ear is highlighted in

both pictures.

CT images. The majority of these models predict clin-

ically relevant image features from cardiac CT, such

as coronary artery calciﬁcation scoring (Isgum et al.,

2012; Wolterink et al., 2015; Zeleznik et al., 2021),

non-calciﬁed atherosclerotic plaque localization (Ya-

mak et al., 2014; Zhao et al., 2019), and stenosis from

cardiac CT (Lee et al., 2019; Zreik et al., 2019).

Almonacid-Uribe, S., Santana, O., Hernández-Sosa, D. and Freire-Obregón, D.

Deep Learning for Diagonal Earlobe Crease Detection.

DOI: 10.5220/0011644400003411

In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2023), pages 74-81

ISBN: 978-989-758-626-2; ISSN: 2184-4313

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Even though the development of DL on CT im-

ages is promising, CT equipment is expensive and

cardiac illnesses are hard to ﬁnd unless the patient

has symptoms and goes to the hospital for a cardiac

checkup. In this context, the diagonal earlobe crease

(DELC) can be a helpful guide to identify cardiac

problems. This crease extends diagonally from the

tragus to the earlobe’s border (see Figure 1). It is

also known as Frank’s Sign because it was ﬁrst de-

scribed by Frank in a case series of CAD patients

(Frank, 1973). Since then, numerous reports have

been published concerning its association primarily

with atherosclerosis, particularly CAD (Wieckowski,

2021). While not as well known as more traditional

approaches, DELC examinations are painless, non-

invasive, and simple to interpret. If its diagnostic ac-

curacy is sufﬁcient for decision-making, it could be

utilized in primary care or emergency departments.

In this work, we have created a DELC detector

using state-of-the-art (SOTA) backbones and ear col-

lections as benchmarks for the models. First, we have

gathered DELC ear images available on the Internet.

Then, we developed multiple DL models consider-

ing pre-trained encoders, also known as backbones,

to predict whether or not an ear displays a DELC. In

addition, we analyzed the performance of the consid-

ered backbones by varying the classiﬁer parameters

and found no correlation between the number of pa-

rameters and the best model.

Our proposal was evaluated using a mixed-source

dataset. As previously stated, we gathered 342 pos-

itive DELC images by collecting publicly available

images from the Internet and cropping off the ears.

Negative samples were obtained from a publicly ac-

cessible ear database, namely AWE (Emer

c et al.,

2017). All images are collected from natural settings,

including lighting, pose, and shape variations. Con-

sidering the number of samples, data augmentation

techniques were considered during training. The out-

comes are remarkable (predictions up to 97.7% accu-

rate) and have yielded intriguing insights.

Even though the earlobe is a relatively small

part of the ear (see Figure 2), the classiﬁer’s perfor-

mance is noteworthy. Unlike other diseases, such as

melanoma, which can be found anywhere in the hu-

man body, the DELC is located in a speciﬁc area,

facilitating the detection task. The usability of the

trained models reveals an additional insightful rev-

elation. Light-weight convolutional neural networks

such as MobileNet provide high accuracy, balancing

the precision of complex neural network structures

with the performance constraints of mobile runtimes.

Hence, ubiquitous applications could take advantage

of this proposal, making it possible to detect DELC by

Helix

Tragus

Lobule

Anti-Helix

Concha

Figure 2: Outer ear scheme. The human earlobe (lobulus

auriculae) is composed of areolar and adipose connective

tissues that lack rigidity. Due to the absence of cartilage in

the earlobe, it has an abundant blood ﬂow and may aid in

warming the ears (Steinberg and Rosner, 2003).

just using a smartphone anywhere and anytime. Our

contributions can be summarized as follows:

• We propose a novel dataset with DELC ear im-

ages in the wild with 342 samples. All samples

have been gathered from the Internet. The dataset

is publicly available.

• We experimentally demonstrate that it is possible

to tackle this problem by combining pre-trained

backbones with a new classiﬁer.

• In this experiment, eleven different backbones are

compared to one another regarding their DELC

detection performance. Moreover, the models’

size-performance trade-off analysis demonstrates

that the problem can be effectively addressed by

employing light-weight encoders. As aforemen-

tioned, this opens the door for the broad imple-

mentation of this technology.

The remainder of this paper is organized as fol-

lows. Section 2 discusses previous related work. Sec-

tion 3 describes the proposed pipeline. Section 4 re-

ports the experimental setup and the experimental re-

sults. Finally, conclusions are drawn in Section 5.

2 RELATED WORK

The state of the art can be studied from both phys-

iological and technological viewpoints. The former

aims to ﬁnd support for the relationship between CAD

and DELC by examining related studies, while the lat-

ter intends to evaluate the Computer Vision Commu-

nity proposals.

Deep Learning for Diagonal Earlobe Crease Detection

2.1 Physiological Relevance

Several investigations over the previous few decades

have established an association between DELC and

cardiac issues. DELC is a unilateral or bilateral diag-

onal fold in the earlobe, typically making a 45-degree

angle from the intertragic notch to the posterior edge

of the ear. This marker has a grading system linked to

the incidence of CAD based on numerous character-

istics, including length, depth, bilateralism, and incli-

nation. Complete bilateralism is regarded as the most

severe condition (Rodr

ıguez-L

opez et al., 2015).

As previously stated, Frank established the ini-

tial idea for this association (Frank, 1973). Accord-

ing to some scientists, it indicates physiological aging

(Mallinson and Brooke, 2017). Nonetheless, the CAD

concept began to gather support, and several addi-

tional researchers conducted experiments demonstrat-

ing that this link can accurately predict if a patient is

prone to cardiovascular issues. It should be noted that

coronary disease is one of the leading causes of death

in developed nations (Sanchis-Gomar et al., 2016);

hence, early detection is essential for enhancing the

patient’s quality of life and preventing or lowering

CAD-related mortality.

A pioneer work evaluated 340 patients, of whom

257 had CAD (Pasternac and Sami, 1982). It was de-

termined that 91% of patients with DELC had CAD,

the most prevalent sign in those with more severe

disease. More recently, Stoyanov et al. investi-

gated 45 patients, 16 females and 29 males (Stoy-

anov et al., 2021). Twenty-two individuals had a

well-documented clinical history of CAD, while the

remaining patients did not. Upon general examina-

tion before the autopsy, 35 patients had well-formed

DELC. In addition, patients with pierced ears had no

signs of lobule injury due to piercing. Hence the ob-

served creases were accepted as DELC.

2.2 Computer Vision Relevance

For decades, biometric traits have been explored in

Computer Vision. Recent research has focused on

gait analysis or body components to address a va-

riety of tasks, including violence detection (Freire-

Obreg

on et al., 2022), facial expressions (Santana

et al., 2022), face/voice veriﬁcation (Freire-Obreg

et al., 2021), and forensics (Castrill

on-Santana et al.,

2018). Ear recognition has also been widely ad-

dressed (Gald

amez et al., 2017; Alshazly et al., 2019).

For healthcare use, relevant proposals diagnose

several ear-related diseases such as otitis media, attic

retraction, atelectasis, tumors, or otitis externa. These

categories encompass most ear illnesses diagnosed by

DELC_ULPGC

(Positive subset)

AWE'

(Negative subset)

Figure 3: DELC ULPGC+AWE’ Dataset. The studied

dataset comprises two in-the-wild subsets. Both of them

are gathered from the Internet: a subset of the well-known

AWE dataset (Emer

c et al., 2017) as the DELC negative

subset and the new proposed DELC ULPGC subset.

observing the eardrum with an otoendoscopy (Cha

et al., 2019). Recently, Zeng et al. combined several

pre-trained encoders to achieve a 95,59% accuracy on

detecting some of these illnesses using otoendoscopy

images as input (Zeng et al., 2021). These authors ar-

gued that using pre-trained DL architectures provides

a remarkable advantage over traditional handcrafted

methods. To diagnose Chronic Otitis, Wang et al.

proposed a deep-learning system that automatically

retrieved the region of interest from two-dimensional

CT scans of the temporal bone (Wang et al., 2020).

These authors asserted that their model’s performance

(83,3% sensitivity and 91.4% speciﬁcity) was equiva-

lent and, in some instances, superior to that of clinical

specialists (81,1% sensitivity and 88.8% speciﬁcity).

We have also adopted a DL approach to tackle the

DELC detection problem. In contrast, we aim to use

the ear as a marker for CAD.

Hirano et al. published an experimental study an-

alyzing DELC and CAD (Hirano et al., 2016). Their

research employed a handcrafted approach (manually

trimmed earlobes and a Canny edge detector) to de-

tect DELC in meticulously captured images. For this

experiment, 88 participants’ ears were photographed

from a single frontal angle. Only 16% of the partici-

pants were healthy. Unlike this study, we considered

images of ears in the wild, which varied greatly in

pose and illumination.

ICPRAM 2023 - 12th International Conference on Pattern Recognition Applications and Methods

AWE

DELC

ULPGC

shape

properties

AWE'

Pre-trained

Encoder

Classifier

Prediction

Prep-processing Classification

Figure 4: The proposed pipeline for the DELC detection system. The devised process comprises two main modules: the ear

pre-processing module and the classiﬁcation module. In the ﬁrst module, the DELC ULPGC+AWE’ dataset is generated and

passed to the second module, where features are computed. The resulting tensor acts as an input to the classiﬁer, completing

the process.

3 DESCRIPTION OF THE

PROPOSAL

This paper presents and assesses a sequential training

pipeline consisting of two primary modules: a mod-

ule for the production of datasets and a module imple-

menting a pre-trained backbone followed by a train-

able classiﬁer that computes a distance measure of the

generated embeddings. This distance metric is used to

calculate a loss function that is sent back to the classi-

ﬁer. Figure 4 shows a schematic representation of the

approach described in this paper.

3.1 DELC ULPGC+AWE’ Dataset

To our knowledge, there is no DELC dataset avail-

able to the public. The collection procedure relies on

the idea of generability. We intend to develop robust

detection models for use in the ﬁeld. This collection

of pictures was acquired from the Internet under un-

restricted conditions. Consequently, images exhibit

substantial differences in the pose, scale, and illumi-

nation (see Figure 3). The procedure involves four

steps:

1. Web scraping. We conducted a search using key

terms such as “DELC”, “Frank’s Sign”, and sev-

eral celebrity names to download images.

2. The labeling phase of ears includes trimming the

ear region. The selected tool to perform this task

is labelImg

. We did not differentiate ear sections;

the entire ear was cut off. Unlike earlier hand-

crafted techniques (Hirano et al., 2016), DL al-

gorithms can detect across a larger region. After

the second stage, the subset of 342 positive DELC

images is available.

https://github.com/heartexlabs/labelImg

3. We compute statistical shape properties (mean

and standard deviation) of the positive subset.

This information is later used to obtain the nega-

tive DELC subset. The mean shape of the positive

subset is 82 × 159.

4. We have considered the publicly available AWE

dataset to generate the negative subset (Emer

et al., 2017). The AWE data collection introduces

the concept of ear images captured in the wild.

Emersic et al. collected Internet-based celebrity

photos. Each subject comprises ten photos, rang-

ing in size from 15 × 29 pixels for the smallest

sample to 473 × 1022 pixels for the largest. We

selected this dataset due to the nature of the gath-

ering process, which is identical to ours. To this

end, we extracted images within the resolution of

the DELC-positive subset. Finally, to ensure no

DELC-positive samples were in the selected im-

ages, we examined them to generate a 350-image

subset of negative images, namely AWE’ subset.

3.2 The Proposed Architecture

The implemented encoding transforms the input data

into a vector of features. Initially, each input sample

is sent to the encoder that has been trained to extract

features. These encoders are trained on the ImageNet

dataset with 1000 distinct classes (Deng et al., 2009).

Convolutional layers closest to the encoder’s input

layer learn low-level features such as lines, whereas

layers in the middle of the encoder learn complicated

abstract characteristics. The last layers interpret the

retrieved features within the context of a classiﬁcation

task.

The trainable classiﬁer reﬁnes and condenses the

previously computed features into a smaller, more

speciﬁc set of features. It consists of two dense layers,

Deep Learning for Diagonal Earlobe Crease Detection

each with 1024 units. Finally, a sigmoid activation

function generates the classiﬁcation output.

3.3 The Adopted Experimental Protocol

Data Augmentation. The collection under consider-

ation contains an insufﬁcient number of samples. For

instance, nearly 350 samples per class are inadequate

to train a classiﬁer without overﬁtting. A strategy

for augmenting data yielded 2100 photos per class.

The transformations utilized for data augmentation

include random brightness, random contrast, motion

blur, horizontal ﬂip, shift, scale, and rotate (Buslaev

et al., 2020). Augmented subsets are exclusively uti-

lized for training purposes.

Backbone Comparison. Several pre-trained en-

coders were compared: VGGNet (Simonyan and Zis-

serman, 2015), InceptionV3 (Szegedy et al., 2016),

ResNet (He et al., 2016), Xception (Chollet, 2016),

MobileNet (Howard et al., 2017) and DenseNet

(Huang et al., 2016). As aforementioned, these back-

bones were trained using the ImageNet dataset with

1000 distinct classes (Deng et al., 2009). The pipeline

depicted in Figure 4 paired the considered pre-trained

encoder with a trainable classiﬁer, utilizing the Adam

optimizer (Kingma and Ba, 2015) with a learning rate

of 10

−3

and a decay rate of 0.4.

4 EXPERIMENTAL SETUP

This section is divided into two subsections related to

the setup and results of the designed experiments. The

ﬁrst subsection describes the technical details of our

proposal, such as the loss function and the data split.

The achieved results are summarized in the second

subsection.

4.1 Experimental Setup

As mentioned above, the classical detection scenario

in classiﬁcation aims to determine which class be-

longs to a sample. In this regard, we have considered

two classes: DELC and not DELC. Since it is a bi-

nary classiﬁer, the considered loss function to tackle

the problem is the binary cross-entropy:

Loss = −1/N ∗

∑

i=1

−(y

log(p

) + (1 − y

)log(1 − p

))

(1)

Where p

is the i-th scalar value in the model out-

put, y

is the corresponding target value, and N is the

number of scalar values in the model output.

Figure 5: Relative comparison performance and size of

encoders. The blue line stands for the relative performance

between encoders, whereas the red bars stand for the rela-

tive size of the encoder. The higher the blue line, the better,

meaning a higher performance. The smaller the red bar,

the better, meaning the model is lighter than others with a

higher bar.

Finally, the results presented in this section refer

to the average accuracy on ﬁve repetitions of 9-fold

cross-validation. On average, 615 original samples

are selected for training, and the remaining 69 sam-

ples are used for the test. Contrary to test samples,

the selected training samples are augmented during

training.

Table 1: Absolute comparison of different backbones on

the DELC ULPGC+AWE’ dataset. The table is orga-

nized in terms of backbone, validation accuracy (Val. Acc.),

test accuracy (Test Acc.), and the number of parameters of

the backbone (#B

Param

). The bold entries show the best re-

sult and the lightest model.

Backbone Val. Acc. Test Acc. #B

Param

Xception 95,1% 94,1% 22.9M

VGG16 96,5% 93,9% 138.4M

VGG19 95,1% 92,7% 143.7M

ResNet50 98,1% 95,8% 25.6M

ResNet101 97,5% 94,8% 44.7M

ResNet152 97,8% 95,1% 60.4M

MobileNet 98,7% 96,7% 4.3M

InceptionV3 98,9% 9

7% 23.9M

DenseNet121 96,4% 95,5% 8.1M

DenseNet169 88,7% 88,1% 14.3M

DenseNet201 95,1% 93,4% 20.2M

ICPRAM 2023 - 12th International Conference on Pattern Recognition Applications and Methods

4.2 Results

Table 1 provides a summary of the results obtained of

the considered backbones. Validation and test accu-

racy are provided to demonstrate the absence of over-

ﬁtting. As expected, validation accuracy exceeds test

accuracy by 1% to 3%. Adding additional layers or

epochs did not improve these results. The table also

displays the size of each backbone in millions of pa-

rameters. As can be seen, the backbone’s size does

not affect the model’s robustness.

Observing the table in detail, on the one side, it is

evident that almost all pre-trained encoders perform

remarkably detecting DELC. InceptionV3 achieves

the best result (97.7%), whereas DenseNet169

achieves the poorest (88.1%).

Figure 5 better explores the relationship between

backbones by displaying the relative performance

and size. From the robustness standpoint, it is

clear that InceptionV3 is more than 11% superior to

DenseNet169, the poorest backbone. VGG19 and

VGG16 are the most prominent models in size, while

the others fall between 20% and 40% below that size.

Figure 5 also highlights the relative signiﬁcance

of size when addressing the DELC issue. As can

be seen, the smaller encoder within each family

usually provides a superior performance. For in-

stance, from the DenseNet models, DenseNet121

delivers a better outcome. The same is true for

VGG models (VGG16>VGG19) and ResNet models

(ResNet50>ResNet152>ResNet101).

The two top models support the most pertinent

insight. An absolute difference of 1%, but the Mo-

bileNet encoder is ﬁve and a half times smaller than

the InceptionV3 encoder. In this regard, MobileNet

performs three times faster than InceptionV3. How-

ever, a further error analysis of their performance is

necessary to determine if their performance is robust

in terms of balance predictions. In this regard, Fig-

ures 6 and 7 provide a closer examination of their

confusion matrices. As can be seen, both models

exhibit quite balanced performance, with the Mo-

bileNet encoder being the most promising due to its

performance-size trade-off.

5 CONCLUSIONS

This paper presents a DELC classiﬁcation analysis

to determine whether or not an ear image contains

Frank’s Sign. Our research has shown a relationship

between the size and performance of encoders. The

reported experiments revealed that ear images could

be used to sufﬁciently complete this task. Contrary

Figure 6: InceptionV3 Confusion Matrix.

Figure 7: MobileNet Confusion Matrix.

to the literature, we have demonstrated that no further

earlobe analysis is necessary.

Unlike prior research, our study focuses on images

in the wild. Our proposal includes the creation of a

positive DELC subset and using an existing dataset

(AWE dataset) to generate a negative DELC subset.

Due to the shared acquisition method, both datasets

can be considered valid for the task at hand: images

from the Internet in unconstrained environments. In

addition, data augmentation techniques are required

during training due to subset-size limitations.

We have effectively addressed this complex im-

age scenario that requires interpretation based on en-

coder performance and size. The exploited encoder

provides remarkable accuracy on the problem. How-

ever, we have shown that light-weight encoders usu-

ally perform better within the same backbones’ fam-

ily (ResNet, DenseNet, VGG). Moreover, due to

their performance-size trade-off (-1% performance,

x3 times faster and x5.5 times lighter), we suggest the

MobileNet as the most promising encoder.

Deep Learning for Diagonal Earlobe Crease Detection

This line of research presents numerous intrigu-

ing opportunities in healthcare scenarios. This pro-

posal presents an opportunity to optimize pathways

of diagnosis and prognosis and develop personalized

treatment strategies by creating and utilizing larger

datasets. Furthermore, analyzing pictures of earlobes

for non-invasive DELC detection is among the most

important applications. Besides, monitoring ears dur-

ing aging is possible and may provide patient-speciﬁc

insight into current health and alert medical staff to

risk situations.

ACKNOWLEDGEMENTS

We want to acknowledge Dr. Cecilia Meiler-

Rodr

ıguez for her creative suggestions and inspiring

ideas. This work is partially funded by the ULPGC

under project ULPGC2018-08, the Spanish Ministry

of Economy and Competitiveness (MINECO) under

project RTI2018-093337-B-I00, the Spanish Ministry

of Science and Innovation under projects PID2019-

107228RB-I00 and PID2021-122402OB-C22, and

by the ACIISI-Gobierno de Canarias and European

FEDER funds under projects ProID2020010024,

ProID2021010012 and ULPGC Facilities Net and

Grant EIS 2021 04.

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Deep Learning for Diagonal Earlobe Crease Detection