11institutetext: Department of Computer Languages and Computer Science.
University of Málaga, Bulevar Louis Pasteur, 35, Málaga, Spain, 29071
22institutetext: Cardiology Deparment, Hospital Universitario Virgen de la
Victoria, Málaga, 29010, Spain
33institutetext: Instituto de Investigación Biomédica de Málaga y Plataforma
en Nanomedicina-IBIMA Plataforma BIONAND, C/ Severo Ochoa, 35, Málaga
TechPark, Campanillas, 29590, Málaga, Spain
44institutetext: Centro de Investigación Biomédica en Red de Enfermedades
Cardiovasculares (CIBERCV), Instituto de Salud Carlos III (ISCIII), Avenida
Monforte de Lemos, 3-5. Pabellón 11. Planta 0, 28029, Madrid, Spain
55institutetext: Facultad de Medicina, University of Málaga, Bulevar Louis
Pasteur, 37, 29071, Málaga, Spain

# CADICA: a new dataset for coronary artery disease detection by using
invasive coronary angiography

Ariadna Jiménez-Partinen 1133 Miguel A. Molina-Cabello 1133 Karl Thurnhofer-
Hemsi 113344 Esteban J. Palomo 1133 Jorge Rodríguez-Capitán 223344 Ana I.
Molina-Ramos 223344 Manuel Jiménez-Navarro 22334455

###### Abstract

Coronary artery disease (CAD) remains the leading cause of death globally and
invasive coronary angiography (ICA) is considered the gold standard of
anatomical imaging evaluation when CAD is suspected. However, risk evaluation
based on ICA has several limitations, such as visual assessment of stenosis
severity, which has significant interobserver variability. This motivates to
development of a lesion classification system that can support specialists in
their clinical procedures. Although deep learning classification methods are
well-developed in other areas of medical imaging, ICA image classification is
still at an early stage. One of the most important reasons is the lack of
available and high-quality open-access datasets. In this paper, we reported a
new annotated ICA images dataset, CADICA, to provide the research community
with a comprehensive and rigorous dataset of coronary angiography consisting
of a set of acquired patient videos and associated disease-related metadata.
This dataset can be used by clinicians to train their skills in angiographic
assessment of CAD severity, by computer scientists to create computer-aided
diagnostic systems to help in such assessment, and to validate existing
methods for CAD detection. In addition, baseline classification methods are
proposed and analyzed, validating the functionality of CADICA with deep
learning-based methods and giving the scientific community a starting point to
improve CAD detection.

###### Keywords:

Invasive coronary angiography dataset cardiovascular artery disease
classification deep learning medical images.

##  1 Introduction

Coronary artery disease (CAD) remains the leading cause of death globally [1,
2]. Clinical presentations of CAD are currently categorized as either acute
coronary syndromes or chronic coronary syndromes, and assessing patients with
suspected CAD is a significant component of healthcare costs [3]. Invasive
coronary angiography (ICA) is considered the gold standard of anatomical
imaging evaluation when CAD is suspected [4, 5]. ICA acquisition is based on
introducing radiocontrast through a catheter inserted by a percutaneous
incision in the femoral or brachial artery, situated in the groin and the arm,
respectively. The radiocontrast agent enhances the visibility of coronary
arteries, with cardiac angiography equipment, X-ray-based, the state of the
arteries is shown, allowing the clinicians to evaluate it and conclude if
there is a luminal obstruction. The presence of obstructive CAD in ICA,
usually defined as a lesion greater than 70 percent, has been recognized as an
unequivocal sign of a bad cardiovascular prognosis. In contrast, it was
initially proposed that non-obstructive CAD (usually defined as a lesion less
than 70 percent) could constitute a condition related to a good cardiovascular
prognosis [6], but subsequent evidence has increasingly shown that it confers
an adverse prognosis when compared to the prognosis in the absence of CAD [7,
8, 9]. Consequently, it is currently accepted that cardiovascular risk
increases when the degree of stenosis increases. However, risk evaluation
based on ICA has several limitations. There is enough evidence indicating that
the visual assessment of stenosis severity alone has significant interobserver
variability, so this visual assessment alone does not provide us with enough
information upon which to base decisions about revascularization in many
patients [10]. In addition to this, angiographic assessment of CAD severity is
limited in providing consistent information regarding the physiological
significance of coronary lesions. Angiography is especially limited in
coronary stenoses of intermediate severity (40–70 percent obstruction), where
it predicts functional significance in less than 50 percent of lesions [11].
Visual assessment of coronary angiography fails to adequately determine lesion
significance because lumen stenosis is only one variable out of many that
influence the flow limitation of coronary lesions [12]. Lesion length,
collateral flow, and the amount and health of the myocardial bed supplied are
other essential factors that are not readily assessed by coronary angiography
[13]. In order to overcome the aforementioned limitations, current guidelines
recommend the routine assessment of vessel physiology in the form of indices
derived from invasive pressure wire, such as fractional flow reserve and the
instantaneous wave-free ratio [4, 5]. Despite these recommendations, the
implantation of these functional tests in clinical practice has been
especially low [14].

Many medical image datasets have been provided to the research community with
the aim of developing an algorithm that can serve as a computer-aided
diagnosis system [15, 16, 17, 18]. However, there is a lack of available and
high-quality open-access datasets regarding ICA images because most related
studies use private image sets [19]. Some are provided by an associated
medical center and used for image segmentation tasks [20, 21], while others
are focused on detection and classification [22, 23]. None of them provides
access to other researchers to their data, which is necessary to achieve
advances in this field. The main contributions of this work can be listed as
follows:

  * •

To provide the research community with a comprehensive and rigorous scientific
coronary angiography dataset formed by a set of videos acquired from patients
and metadata related to diseases associated with them. This dataset may serve
medical doctors to train their skills in angiographic assessment of CAD
severity, and computer scientists to create computer-aided diagnosis systems
to help with that kind of evaluation and to validate and improve existing
methods for CAD detection by training them on more data.

  * •

A set of resources for testing algorithms. Additionally, an exhaustive
revision and expansion process of these tools will be carried out regularly.

  * •

To provide a study of the performance of known architectures using the dataset
with the aim of classifying ICA images according to the presence of lesions.

  * •

To help the community to identify other related challenges to provide a focus
for future research.

The rest of the paper is structured as follows: Section 2 describes the recent
state-of-art works related to ICA. In Section 3, the most important details
about the creation and organization of the CADICA dataset are given. The
experimental results are shown in Section 4. A discussion is provided in
Section 5. Finally, Section 6 is devoted to conclusions.

##  2 Related works

Deep learning has been thoroughly used for both classification and
segmentation tasks in medical imaging, including in the area of cardiology,
where the most common imaging modalities are MRI (magnetic resonance imaging),
and X-ray-based, such as CCTA (Coronary Computed Tomography Angiography) and
ICA (Invasive Coronary Angiography) [24].

Specifically for ICA images, we found the work of Auet al. [25], which uses
ICA images of the right coronary artery to detect and classify coronary
stenosis. They implemented a complex method based on three phases with
different networks to do it. First, they used a detection network, YOLONet, to
localize the patch where is the lesion. Straightaway, the lesion is segmented
using the U-Net model, and finally, it is classified by a small CNN as
stenosis if the narrowing is higher than 70%.

Nasr-Esfahani et al. [20] used patches from 44 coronary angiographies to set a
system, composed of two CNNs based on learned kernels, which returns a
segmentation probability map of the ICA images.

Wu et al. [26] proposed a method to localize stenosis lesions in ICA image
sequences based on three stages. For this study, 148 sequences of ICA images
were employed. Firstly, the most appropriate frames from the complete sequence
are selected using the U-Net architecture. Next, a deconvolutional single-shot
multibox detector localizes the possible boxes that contain stenosis, and
finally, they designed a temporal module to determine which boxes are actually
stenosis, considering the temporal sequence of the boxes selected.

Zhang et al. [27] used the U-net architecture to implement the C-Unet model
that is a deep learning-based solution to automatically extract the
centerlines of blood vessels from ICA images, achieving a precision higher
than 80%.

Cong et al. [23] implemented a system completely automatically for the
classification and location of ICA images, which had been clustered into the
left coronary artery and right coronary artery, and the lesions were into
categories depending on their grade. To start, an inception-v3 model
classified the images according to the projection to which they belonged.
Then, the ideal candidate frames were selected by a fusion between
inception-v3 and LSTM (long-short-term memory), these candidates were
classified into predefined categories by another inception-v3 model adapted
for it, and to conclude, they employed a class activation map to identify the
regions in ICA images where the lesion could be located.

Zhou et al. [22] proposed a method to classify ICA images with stenosis. To
carry out the study, they employed 8731 right coronary artery images, and the
procedure was formed by three stages. Firstly, by ResNet-18 structure was
carried out to extract keyframes, which presents enough contrast and clarity,
from video sequences, there were 6533 non-key frames and 2198 keyframes
Secondly, a vessel segmentation was implemented by a U-Net model to get their
masks. And thirdly, lesions were measured by the skeletonization of the
vessels in masks.

Moon et al. [28] classified the ICA images into normal and abnormal arteries
if the narrowing is lower or higher than 50%, respectively. As in the previous
work reported, in this study, the first phase was based on extracting key
frames from sequences of ICA videos, 542 videos were used, and then, an
inceptionv3 architecture was fed with these keyframes to can classify them
into normal or abnormal. To finalize, stenosis lesions were visually localized
using class activation mapping.

These reported works have some aspects in common with the study that we had
been carrying out, but most of them are focused on implementing segmentation
or using patches to classify the images, instead of complete images. Also,
some of them excluded images with more than one lesion or only classify
obstructive lesions, they utilize smaller datasets, most of them are private
datasets, the detail of the artery and projections used is omitted or a
different annotation is implemented.

##  3 CADICA dataset

Next, the most important details about the creation and organization of the
CADICA dataset are presented.

###  3.1 Patient Selection

The dataset proposed in this work consists of 668 invasive coronary
angiography videos from 42 patients, acquired at Hospital Universitario Virgen
de la Victoria, Málaga, Spain. They have been included within the regulation
set by the local ethical committee of the hospital and patient consent was
waived, because this is a retrospective study with anonymized data.
Prerequisites and data selection have not been performed in order to impose
clinical fidelity. Therefore, a wide variability of cases and acquisition
configurations were implied, while some cases were laborious to tag. This way,
the dataset exhibits a high variety of pathological cases and image quality.
Table 1 summarizes the baseline and demographic characteristics of patients
included in the dataset.

Table 1: Demographic and baseline characteristics of the patients. Data are given as % or as median (interquartile range) Age (years) | 71.5 (58.25-78)
---|---
Sex (female-male) | 47.6% - 52.4%
Diabetes mellitus | 40.5%
Dyslipidemia | 40.5%
Smoker | 45.2%
High blood pressure | 61.9%
Kidney failure | 14.3%
Heart failure | 14.3%
Atrial fibrillation | 4.8%
Left ventricular ejection fraction
Normal (ejection fraction $>$55%) | 68.2%
Mild dysfunction (ejection fraction 45%-55%) | 9.8%
Moderate dysfunction (ejection fraction 45%-35%) | 0%
Severe dysfunction (ejection fraction $<$35%) | 22%
Clinical indication for angiography
Chronic coronary syndrome | 4.9%
Non-ST segment elevation acute coronary syndrome | 65.9%
ST segment elevation acute coronary syndrome | 29.3%
Number of vessels affected
0 | 23.8%
1 | 50%
2 | 14.3%
3 | 11.9%
Maximum degree of the coronary artery involvement
$<$20% | 14.3%
20-50% | 66.7%
$>$70% | 19%

###  3.2 Acquisition Protocol

The invasive coronary angiography videos were acquired as Digital Imaging and
Communication in Medicine (DICOM) files recorded at 10 frames per second and
with different duration (4-8 seconds) depending on the projection used, but
they were converted to PNG images for effortless management. The frame size of
each video is 512 $\times$ 512 pixels, while the length of the videos varies
from 1 to 151 frames. The cardiac angiography equipment used was Artis Zee
(Siemens AG, Muenchen, Germany). The dose of radiation administered in each
projection ranges between 5-50 mGy. The protocol normally used in each
angiography included five projections for the left coronary artery (LCA), such
as right anterior oblique (RAO) and left anterior oblique (LAO), both with
cranial and caudal angulation, with some additional projections in case of
diagnostic difficulties. The projections used for the right coronary artery
(RCA) are LAO and RAO, with cranial and caudal angulation.

Fig. 1 and 2 show examples of projections for the left coronary artery and the
right coronary artery, respectively.

(a) LAO 45º CAU 35º

(b) LAO 45º CAU 25º

(c) RAO 30º CRA 20º

(d) RAO 28º CAU 15º

Figure 1: Examples for left coronary artery (LCA).

(a) LAO 35º CAU 5º

(b) LAO 30º CRA 25º

(c) LAO 25º CRA 20º

(d) RAO 30º

Figure 2: Examples of projections for right coronary artery (RCA).

###  3.3 Label Protocol

A team of cardiologists was involved in the annotation of the dataset,
assisted by computer scientists. For each frame, those regions of interest are
delimited by a bounding box and classified into categories. This way, for each
region of interest, is provided the location of the top left corner of its
bounding box, its width, its height, and the label of that region of interest.
The possible categories are itemized in Table 2.

Table 2: Categories into which lesions have been divided. Label | Lesion range
---|---
p0_20 |  $<$20%
p20_50 | [20%, 49%]
p50_70 | [50%, 69%]
p70_90 | [70%, 89%]
p90_98 | [90%, 98%]
p99 | 99%
p100 | 100%

For each video, a selection of keyframes is carried out. This selection
contains the list of frames that exhibit a contrast with enough appearance in
order to classify the patient correctly. Videos are organized by patients,
where a certain number of videos have been collected for each patient.
According to their coronary artery stenosis percentage, patients are grouped
into three different categories: $<$ 20% (mild), 20 - 50% (moderate), and $>$
70% (severe). Lesions of 100% imply a total occlusion of the vessel, while a
99% lesion presents a gap where the radiocontrast is imperceptible, but the
continuation of the vessel is visible. Those lesions that had a narrowing
between 50 to 70 percent are classified as obstructive in some studies [29]
and non-obstructive in others [30], while lesions with a higher narrowing than
70 percent in the previous bibliography showed consensus that they should be
taken as obstructive and classify as non-obstructive the lesions that are
solidly taken as non-obstructive (20–50 per-cent). Fig. 3 exhibits some sample
images from different patients according to their classification into these
categories.

(a) Mild

(b) Moderate

(c) Severe

Figure 3: Samples of the 3 categories in which lesions are classified and
delimited by a bounding box annotated.

###  3.4 Dataset Organization

####  3.4.1 Video Selection

The presented dataset provides a total of 668 ICA videos. However, in some
frames of these videos where CAD is present, the lesion is indiscernible,
being difficult to use them for diagnosis. In order to obtain the best videos
for CAD classification, a selection of 382 videos was performed. These videos
have been chosen by the medical team, where CAD can be visually classified
correctly. Thus, videos in which radiocontrast does not perfuse have not been
selected for the classification task. The specifications of CADICA dataset are
reported in Table 3, where the number of patients, videos, images from
selected videos and labels from “lesion” images are itemized. Please note that
the number of labels is higher than the number of “lesion” images since it can
be more than one lesion in an image.

Table 3: Specifications of CADICA dataset: number of patients, videos, images from selected videos and labels from “lesion” images. Patients | 42 | Total
---|---|---
Videos | Non-Selected | 286 | 668
Selected | 382
Images | Non-Lesion | 2,130 | 6,126
Lesion | 3,996
Labels | p0_20 | 1,944 | 6,161
p20_50 | 1,128
p50_70 | 999
p70_90 | 893
p90_98 | 930
p99 | 63
p100 | 204

####  3.4.2 Metadata

The dataset also provides additional clinical data associated with each
patient, such as if the patient suffers from diabetes, dyslipidemia, smoking,
hypertension, another comorbidity (such as chronic obstructive pulmonary
disease), renal insufficiency, heart failure, atrial fibrillation, or left
ventricular ejection fraction. Other information such as age, gender, height,
weight, and later event (such as non-cardiac death or heart attack) is also
reported.

####  3.4.3 Structure

The CADICA dataset becomes a directory that contains the metadata.xlsx file,
which is the file where the clinical data is located, as well as two main
folders that differentiate the videos selected by the medical team for each
patient: nonselectedVideos and selectedVideos. Inside each folder, there are
several sub-directories with the naming convention pX where X is the ID of
each patient, and vY, where Y is the ID of the video of that patient.

The folder pX contains the following information:

  * •

vY: several sub-directories with the videos selected for that patient.

  * •

lesionVideos.txt: contains the IDs of the selected videos where appears at
least one lesion which is labeled.

  * •

nonlesionVideos.txt: contains the IDs of the selected videos where there are
no visible lesions.

The folder vY contains the following information:

  * •

input: a sub-directory containing a separate PNG file for each frame of the
video.

  * •

pX_vY_selectedFrames.txt: contains the IDs of the keyframes for the medical
team, for all the selected videos.

  * •

groundtruth: a sub-directory available only if there are lesions in that
selected video.

The folder groundtruth contains the following information:

  * •

pX_vY_000ZZ.txt: contains the bounding boxes and their category in each row.
There are such files as frames in pX_vY_selectedFrames.txt. Bounding boxes are
specified in the format $[x,y,w,h]$, where $(x,y)$ are the pixel coordinates
of the top left corner, $w$ is the width and $h$ is the height of the bounding
box.

  * •

pX_vY_groundTruthTable.mat: contains a table with the ground truth information
of that video.

##  4 Experiments

Given the provided dataset in this work, we did an exhaustive performance
comparison to classify ICA images according to the presence of lesions, being
a binary problem, where the method classifies between “non-lesion” or “lesion”
(images with any label from Table 2).

###  4.1 Evaluation metrics

In order to measure the performance of a method that classifies coronary
angiography images according to their coronary artery stenosis percentage,
several well-known metrics have been proposed.

Let us consider the true positives or number of hits (TP), true negatives or
correct rejections (TN), false negatives or misses (FN), and false positives
or false alarms (FP). The selected metrics and their definitions are as
follows:

$Acc=\frac{TP+TN}{TP+FP+FN+TN}\quad Fm=2\cdot\frac{PR\cdot RC}{PR+RC}$ (1)
$Bal=\frac{RC+SP}{2}\qquad SP=\frac{TN}{FP+TN}$ (2) $RC=\frac{TP}{TP+FN}\qquad
PR=\frac{TP}{TP+FP}$ (3)

The most representative measures are the Accuracy (Acc), the F-measure (Fm,
also known as F1 score), and the Balanced Accuracy (Bal), which provide a good
overall evaluation of the performance of a given method. All these measures
represent the percentage of hits of the system by providing values in the
interval $[0,1]$, where higher is better.

Meanwhile, other measures are also implicitly considered such as the precision
(PR), the recall (RC), and the specificity (SP). In order to analyze these
metrics, FN must be considered against FP (lower is better), PR against RC
(higher is better).

###  4.2 Methods

####  4.2.1 Convolutional Neural Networks

In this study, different Convolutional Neural Networks (CNNs) are used to
compare their performance to classify ICA images from CADICA into two classes,
“lesion”, which means that appears at least one lesion, and “non-lesion”.

CNN is a type of deep learning model incorporating at least one convolutional
layer, whose purpose is to extract the features from the input image,
triggering under a specific condition [31]. CNNs have become successful
methods with great versatility of applications in several areas, including
medical images, and to solve different problems, such as segmentation,
localization, or classification. Also, CNNs are characterized by their
transferability of knowledge by applying the transfer learning technique,
which is based on employing classification models trained on large datasets,
also named pre-trained networks, which are re-trained with a specific dataset
to specialize them to the particular problem [32]. In this study no layer was
frozen, so all weights were updated according to the input dataset
information. Five known pre-trained CNN architectures are used in this study:

The Residual Networks (ResNets) family [33] introduces the residual connection
to the model, these shortcut connections allow skipping some layers in the
process. In particular, in this study ResNet-18 and ResNet-50 networks are
used, which are characterized by being composed of 18 and 50 layers deep,
respectively.

MobileNet-V2 [34] is a mobile neural network optimized to considerably reduce
the number of parameters, compared with other architectures, which decreases
the computational load. The MobileNet architecture is based on depthwise
separable convolutions, which are a combination of two layers. The first is
depthwise convolution, which applies a single filter to the input without
extracting features, and the second is named pointwise convolution, which
creates a linear combination output with new features [35].

NasNet-Mobile is the smallest version of NasNet models. NasNet models are CNNs
based on Neural Architecture Search (NAS), which consist of basic building
blocks, called cells, optimized by reinforcement learning method [36].

DenseNet-201 is a deep network based on ensuring the maximum information flow
between layers by dense blocks. Dense blocks are blocks of layers, where each
layer is connected to all former layers, instead only to the previous one
[37].

####  4.2.2 Data Preprocessing

In CADICA there are 382 selected videos in total, of which two subgroups were
done differentiating between views of the left (LCA) and right (RCA) coronary
arteries.

The subgroup of LCA views was composed of 216 videos, a total of 3,228 images,
where 1,003 images were labeled as “non-lesion” and 2,225 were labeled as
“lesion”. The subgroup of RCA views consisted of 118 videos, which is 2,077
images in total, whose labels were distributed as 617 images labeled as “non-
lesion” and 1,460 labeled as “lesion”.

The input image of the pre-trained architectures selected is an RGB image of
size 224 $\times$ 224 pixels. Thus, the first processing applied to all images
was to resize them and use the color preprocessing to ensure that images have
the number of channels required, in this case, three channels.

To study the binary classification problem “lesion”/“non-lesion”, both sets
had been divided into training (80%) and test (20%) sets. This division was
done by videos, which means that 80% of the “non-lesion” and “lesion” videos
were used for training and the 20% remaining for testing. This way, frames of
the same video of the train set are unavailable for the test set, because
frames of a video are very similar between them.

Both sets have unbalanced distributions, which can cause the model to
specialize in the majority class, in this case, “lesion”, and be relatively
inefficient at classifying the minority class, in this case, “non-lesion”. To
solve this issue a data augmentation strategy had been applied to the training
sets. This data augmentation was done by using different random basic
operations of the original images, detailed as follows:

  * •

Translations in the x and y axis of [-25,25] pixels randomly, Fig. 4(b).

  * •

Rotation using a random angle between [-25º,25º], Fig. 4(c).

  * •

Scaling of the images with a random scale factor in a range of [0.8,1.7], Fig.
4(d).

(a) Original

(b) Translation

(c) Rotation

(d) Scale

Figure 4: Examples of the modifications applied to the training sets to
augment data.

Modifications were applied to the training sets of both classes, “lesion” and
“non-lesion” images, and to both subsets, LCA and RCA, equalizing and
increasing them. Fig. 5 shows the original class distributions for LCA and RCA
sets, and the final distributions obtained after data augmentation was
implemented, obtaining 3640 and 2342 images of each class in the LCA set and
in the RCA set, respectively.

Figure 5: Class distribution for Left coronary Artery (LCA) and Right Coronary
Artery (RCA) sets.

####  4.2.3 Experimental Setup

Several parameters can be tuned for training convolutional neural networks.
The main ones that we were focusing on are reported below:

  * •

Validation frequency: it is the number of iterations between evaluations of
the training process.

  * •

Maximum number of epochs: indicates the maximum times that the full dataset is
passed to the model to update its weights.

  * •

Optimizer or solver: is the algorithm applied to update the weights of the
network to reduce the loss function.

  * •

Initial Learning Rate: establishes the rate that is going to use to start the
learning procedure.

  * •

Batch size: specifies the number of samples, in this case, images, that are
processed by the model in one iteration.

Due to many options for possible combinations of training parameters, we
started studying the behavior of the performance, establishing some values to
tune the training parameters. To evaluate the progress in the tuning of the
parameters, we used the performance metrics reported in section 4.1, focusing
on F-measure, Balanced Accuracy, and Accuracy, because together they provide a
global view of the performance. The LCA set was employed for this process
because it is the largest and most complex set. Finally, the parameters
selected were used to evaluate the RCA set too.

The first parameters that we set were validation frequency in 50 iterations to
evaluate the training process and the maximum number of epochs established in
10 epochs because the increase of it had an unsubstantial improvement compared
to time-consuming. For the optimizer, we compared different algorithms: Adam
(adaptive moment estimation), SGDM (stochastic gradient descent with
momentum), and RMSProp (root mean square propagation). Besides, we proposed
several rates for the initial learning rate: 0.01, 0.001, 0.0001, and 0.00001.
For the batch size, two values were selected, 16 and 64. In total there are 24
possible combinations, to compare them, we implemented the stratified K-fold
cross-validation. This technique is adequate to evaluate the performance in a
reliable way because the results are averages from different partitions of the
input dataset, so results are independent of the partition employed to
validate.

Table 4: F-measure results obtained on the test set for LCA images using
5-fold stratified cross-validation, five convolutional network architectures,
and different values for batch size, initial learning rate, and optimizer. The
best performances are shown in bold.

Model |  Initial Learning Rate | Batch Size 16 | Batch Size 64
---|---|---|---
_Adam_ | _SGDM_ | _RMSProp_ | _Adam_ | _SGDM_ | _RMSProp_
_MobileNet-V2_ | 0.01 | $0.758\pm 0.033$ | $0.776\pm 0.028$ | $0.755\pm 0.096$ | $0.767\pm 0.036$ | $0.806\pm 0.013$ | $0.720\pm 0.052$
0.001 | $0.798\pm 0.035$ | $0.800\pm 0.025$ | $0.791\pm 0.034$ | $0.812\pm 0.024$ | $0.786\pm 0.018$ | $0.807\pm 0.024$
0.0001 | $0.809\pm 0.030$ | $0.794\pm 0.021$ | $0.818\pm 0.015$ | $0.806\pm 0.016$ | $0.800\pm 0.010$ | $0.808\pm 0.015$
0.00001 | $0.810\pm 0.014$ | $0.785\pm 0.010$ | $\boldsymbol{0.820\pm 0.015}$ | $\boldsymbol{0.810\pm 0.009}$ | $0.766\pm 0.011$ | $0.789\pm 0.012$
_ResNet-18_ | 0.01 | $0.737\pm 0.054$ | $0.739\pm 0.038$ | $0.730\pm 0.094$ | $0.747\pm 0.047$ | $0.789\pm 0.011$ | $0.731\pm 0.065$
0.001 | $0.782\pm 0.020$ | $0.798\pm 0.020$ | $0.765\pm 0.025$ | $0.784\pm 0.013$ | $\boldsymbol{0.794\pm 0.016}$ | $0.762\pm 0.017$
0.0001 | $0.784\pm 0.022$ | $0.780\pm 0.022$ | $\boldsymbol{0.800\pm 0.021}$ | $0.783\pm 0.029$ | $0.788\pm 0.026$ | $0.775\pm 0.014$
0.00001 | $0.783\pm 0.017$ | $0.800\pm 0.029$ | $0.789\pm 0.019$ | $0.772\pm 0.016$ | $0.770\pm 0.016$ | $0.782\pm 0.012$
_ResNet-50_ | 0.01 | $0.655\pm 0.057$ | $0.777\pm 0.027$ | $0.380\pm 0.418$ | $0.755\pm 0.006$ | $0.793\pm 0.013$ | $0.713\pm 0.097$
0.001 | $0.773\pm 0.020$ | $0.774\pm 0.012$ | $0.775\pm 0.031$ | $0.772\pm 0.042$ | $0.779\pm 0.021$ | $0.746\pm 0.055$
0.0001 | $0.785\pm 0.023$ | $0.784\pm 0.025$ | $\boldsymbol{0.823\pm 0.017}$ | $0.806\pm 0.024$ | $0.802\pm 0.016$ | $\boldsymbol{0.818\pm 0.023}$
0.00001 | $0.797\pm 0.037$ | $0.796\pm 0.026$ | $0.808\pm 0.025$ | $0.782\pm 0.040$ | $0.783\pm 0.034$ | $0.777\pm 0.024$
_NasNet-Mobile_ | 0.01 | $0.743\pm 0.018$ | $0.785\pm 0.017$ | $0.712\pm 0.105$ | $0.723\pm 0.037$ | $\boldsymbol{0.798\pm 0.011}$ | $0.717\pm 0.047$
0.001 | $0.807\pm 0.026$ | $\boldsymbol{0.811\pm 0.028}$ | $0.790\pm 0.032$ | $0.790\pm 0.027$ | $0.785\pm 0.019$ | $0.753\pm 0.050$
0.0001 | $0.794\pm 0.025$ | $0.770\pm 0.036$ | $0.803\pm 0.017$ | $0.779\pm 0.015$ | $0.750\pm 0.021$ | $0.783\pm 0.016$
0.00001 | $0.778\pm 0.008$ | $0.714\pm 0.011$ | $0.770\pm 0.016$ | $0.773\pm 0.006$ | $0.659\pm 0.049$ | $0.765\pm 0.010$
_DenseNet-201_ | 0.01 | $0.694\pm 0.020$ | $0.736\pm 0.032$ | $0.666\pm 0.103$ |  |  |
0.001 | $0.761\pm 0.032$ | $0.799\pm 0.019$ | $0.758\pm 0.025$ |  |  |
0.0001 | $0.797\pm 0.036$ | $0.774\pm 0.012$ | $0.801\pm 0.034$ |  |  |
0.00001 | $0.806\pm 0.025$ | $\boldsymbol{0.826\pm 0.017}$ | $0.794\pm 0.014$ |  |  |

Table 5: Balanced Accuracy results obtained on the test set for LCA images
using 5-fold stratified cross-validation, five convolutional network
architectures, and different values for batch size, initial learning rate, and
optimizer. The best performances are shown in bold.

Model |  Initial Learning Rate | Batch Size 16 | Batch Size 64
---|---|---|---
_Adam_ | _SGDM_ | _RMSProp_ | _Adam_ | _SGDM_ | _RMSProp_
_MobileNet-V2_ | 0.01 | $0.586\pm 0.059$ | $0.597\pm 0.052$ | $0.514\pm 0.029$ | $0.608\pm 0.029$ | $0.610\pm 0.024$ | $0.578\pm 0.041$
0.001 | $0.656\pm 0.040$ | $0.644\pm 0.052$ | $0.650\pm 0.044$ | $0.660\pm 0.073$ | $0.630\pm 0.022$ | $0.648\pm 0.052$
0.0001 | $0.667\pm 0.034$ | $0.643\pm 0.035$ | $0.671\pm 0.011$ | $0.665\pm 0.014$ | $0.638\pm 0.030$ | $0.657\pm 0.018$
0.00001 | $0.682\pm 0.020$ | $0.618\pm 0.013$ | $\boldsymbol{0.688\pm 0.024}$ | $\boldsymbol{0.678\pm 0.027}$ | $0.590\pm 0.020$ | $0.662\pm 0.012$
_ResNet-18_ | 0.01 | $0.551\pm 0.071$ | $0.559\pm 0.028$ | $0.492\pm 0.034$ | $0.594\pm 0.053$ | $0.625\pm 0.028$ | $0.582\pm 0.120$
0.001 | $0.609\pm 0.037$ | $0.618\pm 0.023$ | $0.605\pm 0.017$ | $0.636\pm 0.050$ | $\boldsymbol{0.656\pm 0.040}$ | $0.583\pm 0.033$
0.0001 | $0.592\pm 0.014$ | $0.633\pm 0.027$ | $0.623\pm 0.017$ | $0.610\pm 0.037$ | $0.632\pm 0.037$ | $0.583\pm 0.027$
0.00001 | $0.640\pm 0.009$ | $0.633\pm 0.061$ | $\boldsymbol{0.649\pm 0.024}$ | $0.615\pm 0.025$ | $0.618\pm 0.043$ | $0.638\pm 0.014$
_ResNet-50_ | 0.01 | $0.558\pm 0.026$ | $0.592\pm 0.029$ | $0.500\pm 0.000$ | $0.578\pm 0.050$ | $0.632\pm 0.027$ | $0.547\pm 0.084$
0.001 | $0.600\pm 0.022$ | $0.613\pm 0.022$ | $0.564\pm 0.055$ | $0.626\pm 0.014$ | $0.642\pm 0.034$ | $0.577\pm 0.037$
0.0001 | $0.636\pm 0.031$ | $0.653\pm 0.033$ | $0.645\pm 0.041$ | $0.638\pm 0.057$ | $\boldsymbol{0.651\pm 0.022}$ | $0.648\pm 0.030$
0.00001 | $0.659\pm 0.060$ | $0.623\pm 0.039$ | $\boldsymbol{0.679\pm 0.033}$ | $0.650\pm 0.047$ | $0.600\pm 0.066$ | $0.642\pm 0.023$
_NasNet-Mobile_ | 0.01 | $0.562\pm 0.020$ | $0.620\pm 0.048$ | $0.531\pm 0.039$ | $0.568\pm 0.043$ | $0.639\pm 0.018$ | $0.597\pm 0.024$
0.001 | $0.626\pm 0.045$ | $0.686\pm 0.034$ | $0.649\pm 0.053$ | $0.610\pm 0.047$ | $\boldsymbol{0.647\pm 0.034}$ | $0.597\pm 0.049$
0.0001 | $0.625\pm 0.029$ | $0.644\pm 0.048$ | $\boldsymbol{0.649\pm 0.025}$ | $0.637\pm 0.033$ | $0.618\pm 0.035$ | $0.629\pm 0.017$
0.00001 | $0.621\pm 0.010$ | $0.583\pm 0.025$ | $0.629\pm 0.010$ | $0.620\pm 0.013$ | $0.529\pm 0.022$ | $0.607\pm 0.017$
_DenseNet-201_ | 0.01 | $0.537\pm 0.084$ | $0.597\pm 0.067$ | $0.512\pm 0.056$ |  |  |
0.001 | $0.576\pm 0.041$ | $0.666\pm 0.020$ | $0.611\pm 0.033$ |  |  |
0.0001 | $0.623\pm 0.060$ | $0.627\pm 0.022$ | $0.669\pm 0.059$ |  |  |
0.00001 | $0.642\pm 0.037$ | $\boldsymbol{0.672\pm 0.025}$ | $0.638\pm 0.022$ |  |  |

Table 6: Accuracy results obtained on the test set for LCA images using 5-fold
stratified cross-validation, five convolutional network architectures, and
different values for batch size, initial learning rate, and optimizer. The
best performances are shown in bold.

Model |  Initial Learning Rate | Batch Size 16 | Batch Size 64
---|---|---|---
_Adam_ | _SGDM_ | _RMSProp_ | _Adam_ | _SGDM_ | _RMSProp_
_MobileNet-V2_ | 0.01 | $0.659\pm 0.037$ | $0.678\pm 0.041$ | $0.642\pm 0.074$ | $0.674\pm 0.035$ | $0.710\pm 0.017$ | $0.626\pm 0.045$
0.001 | $0.716\pm 0.040$ | $0.713\pm 0.039$ | $0.708\pm 0.044$ | $0.730\pm 0.041$ | $0.696\pm 0.018$ | $0.722\pm 0.033$
0.0001 | $0.729\pm 0.036$ | $0.708\pm 0.029$ | $0.739\pm 0.017$ | $0.726\pm 0.019$ | $0.712\pm 0.018$ | $0.725\pm 0.017$
0.00001 | $0.733\pm 0.018$ | $0.692\pm 0.012$ | $\boldsymbol{0.745\pm 0.021}$ | $\boldsymbol{0.732\pm 0.013}$ | $0.667\pm 0.014$ | $0.708\pm 0.012$
_ResNet-18_ | 0.01 | $0.629\pm 0.069$ | $0.634\pm 0.037$ | $0.611\pm 0.089$ | $0.653\pm 0.055$ | $0.698\pm 0.014$ | $0.634\pm 0.091$
0.001 | $0.687\pm 0.021$ | $0.705\pm 0.025$ | $0.671\pm 0.022$ | $0.697\pm 0.023$ | $\boldsymbol{0.712\pm 0.025}$ | $0.661\pm 0.017$
0.0001 | $0.684\pm 0.021$ | $0.692\pm 0.027$ | $0.708\pm 0.023$ | $0.688\pm 0.036$ | $0.699\pm 0.033$ | $0.673\pm 0.019$
0.00001 | $0.697\pm 0.018$ | $\boldsymbol{0.710\pm 0.043}$ | $0.705\pm 0.022$ | $0.679\pm 0.021$ | $0.679\pm 0.026$ | $0.695\pm 0.015$
_ResNet-50_ | 0.01 | $0.572\pm 0.030$ | $0.678\pm 0.030$ | $0.473\pm 0.209$ | $0.653\pm 0.017$ | $0.703\pm 0.019$ | $0.617\pm 0.064$
0.001 | $0.676\pm 0.023$ | $0.681\pm 0.017$ | $0.650\pm 0.027$ | $0.685\pm 0.043$ | $0.694\pm 0.029$ | $0.646\pm 0.570$
0.0001 | $0.698\pm 0.022$ | $0.702\pm 0.030$ | $\boldsymbol{0.737\pm 0.027}$ | $0.717\pm 0.038$ | $0.718\pm 0.019$ | $\boldsymbol{0.732\pm 0.029}$
0.00001 | $0.715\pm 0.050$ | $0.705\pm 0.031$ | $0.731\pm 0.032$ | $0.699\pm 0.049$ | $0.685\pm 0.050$ | $0.692\pm 0.028$
_NasNet-Mobile_ | 0.01 | $0.638\pm 0.016$ | $0.692\pm 0.028$ | $0.613\pm 0.074$ | $0.623\pm 0.042$ | $\boldsymbol{0.711\pm 0.015}$ | $0.630\pm 0.037$
0.001 | $0.715\pm 0.037$ | $\boldsymbol{0.736\pm 0.035}$ | $0.706\pm 0.040$ | $0.695\pm 0.039$ | $0.701\pm 0.025$ | $0.659\pm 0.056$
0.0001 | $0.704\pm 0.028$ | $0.687\pm 0.044$ | $0.718\pm 0.021$ | $0.693\pm 0.021$ | $0.661\pm 0.028$ | $0.694\pm 0.018$
0.00001 | $0.687\pm 0.008$ | $0.621\pm 0.017$ | $0.682\pm 0.016$ | $0.682\pm 0.007$ | $0.562\pm 0.038$ | $0.670\pm 0.014$
_DenseNet-201_ | 0.01 | $0.589\pm 0.041$ | $0.643\pm 0.047$ | $0.569\pm 0.081$ |  |  |
0.001 | $0.658\pm 0.039$ | $0.719\pm 0.020$ | $0.666\pm 0.030$ |  |  |
0.0001 | $0.705\pm 0.049$ | $0.685\pm 0.015$ | $0.723\pm 0.032$ |  |  |
0.00001 | $0.719\pm 0.034$ | $\boldsymbol{0.746\pm 0.024}$ | $0.706\pm 0.019$ |  |  |

The proposed models were implemented in MATLAB R2022b on a computer system
with an Intel Core i9-10900X processor, 128 GB of RAM, and NVIDIA GeForce RTX
3080 Ti GPU card.

Firstly, we divided the training set of LCA subset into 5 boxes with samples
of both classes, 80% for training the models and 20% as the validation set.
For each model, the 24 combinations established were executed, except
DenseNet-201, which was only trained with batch size 16 because of memory
settings.

The obtained results for test sets, with the different possible combinations
for each architecture, are shown in Tables 4, 5, and 6, where F-measure,
Balanced Accuracy and Accuracy values for each combination are reported. In
these tables, the best results for each model and batch size are shown in
bold.

By observing these tables, we can see how in general terms is better to choose
low learning rates and a batch size of 16 instead of 64. Moreover, SGDM and
RMSProp solvers obtained better results than Adam, especially for batch sizes
of 16, where the results with the Adam solver never overcome SGDM and RMSProp
solvers.

The configurations selected for each model are reported in Table 7. These
configurations were chosen according to the results obtained in Tables 4, 5,
and 6 with 5-fold cross-validation. The optimizer and the initial learning
rate that more times get the highest results were selected. For instance, the
best values for MobileNet-V2 with a batch size of 16 were achieved using the
RMSProp solver and an initial learning rate of 0.00001 in the three tables.
However, some models present different best configurations depending on the
measure taken into account, e.g., ResNet-18 with a batch size of 16 obtained
the best results using the RMSProp optimizer for F-measure and Balanced
Accuracy, whereas the best optimizer was SGDM for Accuracy. In this case, the
RMSProp optimizer is selected. Likewise, the chosen initial learning rate was
0.00001 for ResNet-18.

Table 7: Selected configurations considering F-measure, Balanced Accuracy and Accuracy obtained with 5-fold stratified cross-validation. Model | Batch Size | Optimizer | Initial Learning Rate
---|---|---|---
_MobileNet-V2_ | 16 | RMSprop | 0.00001
64 | Adam | 0.00001
_ResNet-18_ | 16 | RMSprop | 0.00001
64 | SGDM | 0.001
_ResNet-50_ | 16 | RMSprop | 0.00001
64 | RMSprop | 0.0001
_NasNet-Mobile_ | 16 | SGDM | 0.001
64 | SGDM | 0.01
_DenseNet-201_ | 16 | SGDM | 0.00001

###  4.3 Results

The experiments carried out to compare the performance of the different pre-
trained models to evaluate the functionality of the implemented dataset based
on the selected configurations (Table 7) are summarized here. These
configurations were employed to implement a 10-fold stratified cross-
validation, where 90% of images were used for training the models and 10% to
validate the training process. The experimental results using different neural
models, batch size and image subgroups (LCA and RCA) are shown in Table 8,
where Balanced accuracy, F-measure and Accuracy obtained in the test set are
reported. In Table 8 are shown in bold the highest values obtained for each
measure in both subsets. Note that the best results for Balanced Accuracy and
Accuracy were obtained with the MobileNet-V2 model for LCA subset (0.673 and
0.732, respectively). However, for the RCA set the NasNet-Mobile model
achieved the best Balanced Accuracy and Accuracy (0.658, and 0.744). According
to the F-measure, the best results were obtained by the ResNet-50 in both
subsets (0.814, and 0.830, respectively).

To study the best suitable model for these inputs, a ranking was implemented
to evaluate the results attained considering the three measures. The rankings
obtained are reported in Fig. 6, scoring the methods by set and batch size.
The scores were calculated by sorting the obtained values of a measure in
ascending order since a higher value is better according to the considered
measures, meaning that the highest value will be in the last position. The
position indicates the obtained scores. There are five and four methods for
batch sizes 16 and 64, respectively. Therefore, the maximum possible score is
15 points and 12, respectively, indicating that the method attained the
highest values in the three measures.

Focusing on the LCA set, Fig. 6(a) and 6(b), the best outcomes are produced by
MobileNet-V2, obtaining 15 points and 11 points with a batch size of 16 and
64, respectively. Although comparing the results obtained in Table 8, the best
result is produced with a batch size of 64, which reached the maximum balanced
accuracy, 0.673, and the second best F-measure and Accuracy, 0.810 and 0.732,
respectively.

However, in the case of RCA set, Fig. 6(c) and 6(d), ResNet-18 and NasNet-
Mobile are the architectures that obtain higher performance, being NasNet-
Mobile with a batch size of 64 which returns the best Balanced Accuracy and
Accuracy values, 0.658 and 0.744, respectively, and the second best F-measure,
0.826, according to the attained results reported in Table 8.

Table 8: Balanced Accuracy, F-measure, and Accuracy results obtained on the
test set for LCA and RCA images using 10-fold stratified cross-validation,
five convolutional network architectures, and different batch size. The
highest values by columns are shown in bold.

Model |  Batch Size | LCA | RCA
---|---|---|---
Balanced Accuracy | F-measure | Accuracy | Balanced Accuracy | F-measure | Accuracy
_MobileNet-V2_ | 16 | $0.668\pm 0.014$ | $0.805\pm 0.013$ | $0.725\pm 0.015$ | $0.648\pm 0.023$ | $0.811\pm 0.018$ | $0.726\pm 0.023$
64 | $\boldsymbol{0.673\pm 0.026}$ | $0.810\pm 0.020$ | $\boldsymbol{0.732\pm 0.025}$ | $0.641\pm 0.041$ | $0.806\pm 0.026$ | $0.719\pm 0.034$
_ResNet-18_ | 16 | $0.642\pm 0.026$ | $0.796\pm 0.024$ | $0.710\pm 0.029$ | $0.658\pm 0.043$ | $0.825\pm 0.023$ | $0.743\pm 0.031$
64 | $0.632\pm 0.013$ | $0.779\pm 0.009$ | $0.691\pm 0.010$ | $0.624\pm 0.045$ | $0.792\pm 0.031$ | $0.702\pm 0.039$
_ResNet-50_ | 16 | $0.664\pm 0.035$ | $0.793\pm 0.029$ | $0.713\pm 0.036$ | $0.618\pm 0.022$ | $0.799\pm 0.009$ | $0.705\pm 0.013$
64 | $0.645\pm 0.044$ | $\boldsymbol{0.814\pm 0.021}$ | $0.728\pm 0.029$ | $0.620\pm 0.029$ | $\boldsymbol{0.830\pm 0.025}$ | $0.738\pm 0.031$
_NasNet-Mobile_ | 16 | $0.634\pm 0.047$ | $0.789\pm 0.023$ | $0.701\pm 0.033$ | $0.651\pm 0.054$ | $0.804\pm 0.058$ | $0.723\pm 0.067$
64 | $0.637\pm 0.043$ | $0.783\pm 0.020$ | $0.696\pm 0.029$ | $\boldsymbol{0.658\pm 0.034}$ | $0.826\pm 0.033$ | $\boldsymbol{0.744\pm 0.039}$
_DenseNet-201_ | 16 | $0.641\pm 0.024$ | $0.802\pm 0.022$ | $0.715\pm 0.027$ | $0.633\pm 0.037$ | $0.812\pm 0.029$ | $0.723\pm 0.037$

(a) Ranking of LCA set with batch size 16.

(b) Ranking of LCA set with batch size 64.

(c) Ranking of RCA set with batch size 16.

(d) Ranking of RCA set with batch size 64.

Figure 6: Ranking of methods considering F-measure, Balanced Accuracy and
Accuracy obtained for the LCA and RCA subsets using batch sizes 16 and 64.

##  5 Discussion

In this section, some important aspects to be considered of our proposal are
discussed.

  * •

In this work, all degrees of lesions are considered, while other studies only
include severe lesions [25, 38] or exclude those images in which more than one
lesion appears [25]. Nevertheless, our results indicate a fair-to-high
performance, which means that the dataset is functional, but also that this
classification task is complex and challenging.

  * •

Despite the fact that a Balanced Accuracy between 0.65 and 0.67 was obtained,
it represents a good performance since this measure depends on specificity and
recall, which quantifies an average of how correctly both classes are
classified, and the “non-lesion” class is worse classified than the “lesion”
class. This is due to the fact that there are fewer “non-lesion” images than
“lesion” images, although data augmentation was applied to equalize both
classes. Since the “lesion” class includes all degrees of lesion, non-severe
lesions can be incorrectly classified as “non-lesion”.

  * •

Although the DenseNet model usually has a good performance [39, 40, 41], we
could not evaluate it with a batch size of 64 due to memory requirements.
Nevertheless, training this model with a batch size of 16 yielded good results
in terms of F-measure. Therefore, better results will be expected by training
with higher batch sizes.

  * •

Finally, for LCA images, the selected configurations outcomes similar results,
where the MobileNet-V2 was the most suitable model independently of the batch
size. However, the results reported a higher variability for RCA images, since
the most suitable models depend on the batch size. Thus, ResNet-18 is the best
model for batch size 16, whereas for batch size 64 it is the worst model. This
could be because the selected configurations were chosen with 5fold cross-
validation results for the LCA images, and these configurations were applied
to classify the RCA images.

##  6 Conclusions

The coronary dataset published in this work aims to provide the research
community with a conscientious and exhaustive scientific resource. It can
serve as a benchmark for both algorithm implementations and medical staff to
train their abilities on angiographic assessment of CAD severity. Considering
the researchers’ feedback, a set of utilities and the already extensive
dataset will be regularly revised and expanded.

Experiments were designed to try the functionality of the dataset, which was
divided into LCA and RCA images. Five well-known classification architectures
were trained and tested using augmented data to get an overview of the
performance classification of the “lesion” and “non-lesion” images.
Experiments showed that the most suitable models to solve this problem were
MobileNet-V2 for LCA images and NasNet-Mobile for RCA images, getting fair-to-
high outcomes, around 80% F-measure and Accuracy, and 65% Balanced Accuracy.
These results were obtained considering a wide range of lesion levels and
support the idea that this classification task is complex, setting up a
challenge for physicians and computer-aided diagnosis systems. Therefore, the
provided dataset gives the scientific community a starting point to improve
CAD detection.

Given the complexity of the classification problem posed, other architectures
could be tested in order to keep evaluating the performance of different
models. Also, future works will focus on classifying severe lesions images, as
well as trying to identify each type of severity. The use of image patches to
detect and classify the arteries present in this region will help to improve
classification rates.

## Declarations

### Data availability

CADICA dataset is open-access available at the Mendeley Data repository with
the data identification number: 10.17632/p9bpx9ctcv.1, and direct URL to data:
https://data.mendeley.com/datasets/p9bpx9ctcv/1.

### Author Contibutions

All authors listed have made a substantial, direct, and intellectual
contribution to the work, and approved it for publication.

### Declaration of competing interest

The authors declare that the research was conducted in the absence of any
commercial or financial relationships that could be construed as a potential
conflict of interest.

### Conflicts of Interest

The authors declare that they have no conflicts of interest to report
regarding the present study

### Acknowledgment

This work is partially supported by the Autonomous Government of Andalusia
(Spain) under project UMA20-FEDERJA-108, project name Detection,
characterization and prognosis value of the non-obstructive coronary disease
with deep learning, and also by the Ministry of Science and Innovation of
Spain, grant number PID2022-136764OA-I00, project name Automated Detection of
Non Lesional Focal Epilepsy by Probabilistic Diffusion Deep Neural Models. It
includes funds from the European Regional Development Fund (ERDF). It is also
partially supported by the University of Málaga (Spain) under grants
B1-2019_01, project name Anomaly detection on roads by moving cameras;
B1-2019_02, project name Self-Organizing Neural Systems for Non-Stationary
Environments; B1-2021_20, project name Detection of coronary stenosis using
deep learning applied to coronary angiography; B4-2022, project name
Intelligent Clinical Decision Support System for Non-Obstructive Coronary
Artery Disease in Coronarographies; B1-2022_14, project name Detección de
trayectorias anómalas de vehículos en cámaras de tráfico; and by the Fundación
Unicaja under project PUNI-003_2023, project name Intelligent System to Help
the Clinical Diagnosis of Non-Obstructive Coronary Artery Disease in Coronary
Angiography. The authors thankfully acknowledge the computer resources,
technical expertise and assistance provided by the SCBI (Supercomputing and
Bioinformatics) center of the University of Málaga. They also gratefully
acknowledge the support of NVIDIA Corporation with the donation of a RTX A6000
GPU with 48Gb. The authors also thankfully acknowledge the grant of the
Universidad de Málaga and the Instituto de Investigación Biomédica de Málaga y
Plataforma en Nanomedicina-IBIMA Plataforma BIONAND.

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