Indonesian
J
our
nal
of
Electrical
Engineering
and
Computer
Science
V
ol.
32,
No.
1,
April
2025,
pp.
555
∼
568
ISSN:
2502-4752,
DOI:
10.11591/ijeecs.v32.i1.pp555-568
❒
555
HorseNet:
a
no
v
el
deep
lear
ning
appr
oach
f
or
horse
health
classication
Nesrine
Atitallah
1
,
Ahmed
Abdel-W
ahab
1
,
Anas
A.
Hadi
1
,
Hussein
Abdel-J
aber
1
,
Ali
W
agdy
Mohamed
2
,
Mohamed
Elsersy
3
,
Y
usuf
Mansour
1
1
F
aculty
of
Computer
Studies,
Arab
Open
Uni
v
ersity
,
Riyadh,
Saudi
Arabia
2
Department
of
Operations
Research,
F
aculty
of
Graduate
Studies
for
Statistical
Research,
Cairo
Uni
v
ersity
,
Giza,
Egypt
3
Department
of
Computer
Information
Systems,
Higher
Colle
ges
of
T
echnology
,
Al
Ain,
Ab
u
Dhabi,
United
Arab
Emirates
Article
Inf
o
Article
history:
Recei
v
ed
Jan
28,
2024
Re
vised
Oct
15,
2024
Accepted
Oct
30,
2024
K
eyw
ords:
Con
v
olutional
neural
netw
orks
Deep
learning
Horse
wellness
classication
Inception
T
ransfer
learning
V
GG16
ABSTRA
CT
In
equestrian
sports
and
v
eterinary
medicine,
horse
welf
are
is
paramount.
Horse
tiredness,
lameness,
col
ic,
and
anemia
can
be
identied
and
classied
using
deep
learning
(DL)
models.
These
technologies
analyze
horse
images
and
videos
to
help
v
ets
and
researchers
nd
symptoms
and
trends
that
are
hard
to
see
.
Early
detection
and
better
treatment
of
certain
disorders
can
impro
v
e
horses’
health.
DL
models
can
also
impro
v
e
with
ne
w
data,
impro
ving
diagnosis
accurac
y
and
ef
cienc
y
.
This
study
comprehensi
v
ely
e
v
aluates
three
con
v
olutional
neural
net-
w
ork
(CNN)
models
to
distinguish
normal
and
abnormal
horses
using
the
gen-
erated
horse
dataset.
F
or
this
study
,
a
unique
dataset
of
horse
breeds
and
their
normal
and
abnormal
states
w
as
collected.
The
dataset
includes
mobility
pat-
terns
from
this
study’
s
initial
data
collection.
DL
models
lik
e
CNNs
and
trans-
fer
learning
(TL)
models
(visual
geometry
group
(V
GG)16,
InceptionV3)
were
emplo
yed
for
cate
gorization.
T
he
InceptionV3
model
outperformed
CNN
and
V
GG16
with
o
v
er
97%
accurac
y
.
Its
depth
a
nd
multi-le
v
el
structure
allo
w
the
InceptionV3
model
to
recognize
characteristics
in
images
of
v
aried
scales
and
comple
xities,
e
xplaining
its
e
xcellent
performance.
This
is
an
open
access
article
under
the
CC
BY
-SA
license
.
Corresponding
A
uthor:
Ahmed
Abdel-W
ahab
F
aculty
of
Computer
Studies,
Arab
Open
Uni
v
ersity
Riyadh,
Saudi
Arabia
Email:
a.rakha@arabou.edu.sa
1.
INTR
ODUCTION
The
surv
eillance
of
animal
health,
specically
i
n
horses,
is
of
ut
most
importance
in
guaranteeing
the
ir
welf
are,
producti
vity
,
and
economic
signicance
in
both
professional
and
leisure
conte
xts
[1].
Con
v
entional
approaches
to
monitor
horse
health
often
include
labor
-intensi
v
e,
subje
cti
v
e,
and
time-consuming
manual
ob-
serv
ation
and
assessment
conducted
by
v
eterinarians
or
care
gi
v
ers
[2].
There
are
numerous
inherent
limitations
associated
with
traditional
techniques
for
monitoring
the
health
of
horses.
Initially
,
it
is
important
to
note
that
the
process
of
manually
observing
and
e
v
aluating
the
health
of
horses
by
v
eterinarians
or
caretak
ers
is
often
characterized
by
a
signicant
amount
of
labor
and
time
e
xpenditure
[3].
Ho
we
v
er
,
shifts
in
the
e
xpertise
and
background
of
those
caring
for
or
treating
the
horse
can
af
fect
the
reliability
of
these
e
v
aluations.
This
can
lead
to
inconsistencies
when
judging
the
animal’
s
o
v
erall
well-being.
This
may
lead
to
inconsistencies
when
judg-
ing
the
animal’
s
o
v
erall
well-being.
Furthermore,
it
is
important
to
note
that
these
methodologies
may
e
xhibit
limitations
in
identifying
nuanced
alterations
in
indi
viduals’
health
statuses,
including
the
rst
indications
of
J
ournal
homepage:
http://ijeecs.iaescor
e
.com
Evaluation Warning : The document was created with Spire.PDF for Python.
556
❒
ISSN:
2502-4752
ailments
or
injuries.
If
left
undetected
and
untreated
in
a
timely
manner
,
these
illnesses
ha
v
e
the
potential
to
deteriorate
progressi
v
ely
.
These
issues
ha
v
e
the
potential
to
undermine
the
ef
cac
y
of
con
v
entional
procedures,
hence
emphasizing
the
need
for
ne
w
methodologies
that
may
ef
fecti
v
ely
tackle
these
limits
and
enhance
the
precision
and
ef
cienc
y
of
horse
health
monitoring
[4].
The
use
of
cutting-edge
technology
,
such
as
articial
intelligence
(AI),
presents
no
v
el
prospects
for
the
automation
and
enhancement
of
animal
health
monitoring,
hence
enabling
a
more
complete
and
non-intrusi
v
e
approach
to
e
v
aluate
animal
well-being.
Through
the
adop-
tion
of
no
v
el
approaches,
it
is
possible
to
augment
the
well-being,
ef
cac
y
,
and
economic
w
orth
of
horses,
guaranteeing
their
sustained
prosperity
in
both
leisure
and
occupational
conte
xts
[5].
These
technologies
ha
v
e
the
capability
to
automate
and
impro
v
e
the
precision
of
animal
health
mon-
itoring,
therefore
of
fering
a
more
complete
and
non-intrusi
v
e
approach
to
e
v
aluating
animal
well-being.
An
illustration
of
the
use
of
wearable
sensor
s
is
their
utilization
in
the
continuous
monitoring
of
essential
ph
ysio-
logical
indicators,
including
heart
rate,
breathing
rate,
and
temperature.
These
sensors
posses
s
the
capabi
lity
to
communicate
data
in
real-time
to
a
system
based
on
cloud
computing
[6].
This
system
f
acilitates
the
analysis
of
the
data
via
the
use
of
machine
learning
(ML)
algorithms,
thereby
enabling
the
identication
of
the
rst
indications
of
disease
or
damage.
In
a
similar
v
ein,
computer
vision
algorithms
may
be
used
to
e
xamine
video
recordings
of
horses
to
identify
alterations
in
their
g
ait
or
beha
vior
,
which
may
serv
e
as
potential
indicators
of
underlying
health
conditions
[7].
These
adv
anced
technologies
can
impro
v
e
horse
health
monitoring
accurac
y
,
ef
cienc
y
,
and
ef
cac
y
.
T
echnology
can
increase
ani
mal
well
being
and
benet
horse
o
wners,
care
gi
v
ers,
and
v
ets.
The
y
can
pro
vide
timely
and
accurate
horse
health
information
to
help
them
mak
e
informed
horse
care
and
treatment
decisions
[8].
Deep
learning
(DL)
has
the
potential
to
profoundly
transform
the
eld
of
animal
health
monitoring.
W
ith
e
xtensi
v
e
datasets,
DL
algorithms
ha
v
e
the
capacity
to
acquire
kno
wledge
about
patterns
and
then
pro-
vide
precis
e
forecasts
pertaining
to
the
well-being
of
animals
[9].
This
technol
o
gi
cal
adv
ancement
has
se
v
eral
adv
antages
in
comparison
to
con
v
entional
approaches
for
monitoring
animal
well-being,
including
enhanced
precision,
automation,
and
timely
identication
of
health
concerns.
The
use
of
DL
techniques
enables
the
continuous
and
non-in
v
asi
v
e
monitoring
of
animal
health,
hence
f
acilitating
a
more
thorough
e
v
aluation
of
an
animal’
s
o
v
erall
health
condition.
Furthermore,
DL
has
the
capability
to
automate
the
process
of
monitor
-
ing,
therefore
mitig
ating
the
labor
-intensi
v
e
charact
eristics
associated
with
con
v
entional
monitoring
techniques
[10].
The
timely
identication
of
health
concerns
is
of
utmost
importance
in
mitig
ating
the
progression
of
se
v
ere
medical
ailments.
In
this
re
g
ard,
the
use
of
DL
algorithms
may
play
a
pi
v
otal
role
in
promptly
detecting
small
alterations
in
an
animal’
s
health,
hence
f
acilitating
timely
interv
ention
and
treatment.
These
adv
antages
illustrate
the
capacity
of
DL
to
signicantly
transform
the
eld
of
animal
health
monitoring
and
enhance
the
welf
are
of
animals
[11].
T
ransfer
learning
(TL)
is
a
method
of
reusing
a
model
that
has
already
been
trained
on
one
task
to
solv
e
a
ne
w
,
related
task.
This
can
sa
v
e
a
lot
of
time
and
ef
fort,
and
often
leads
to
better
results
than
training
a
ne
w
model
from
scratch,
especially
when
there
is
limited
data
a
v
ailable.
In
f
act,
se
v
eral
w
orks
used
TL
to
classify
images.
Noor
et
al.
[12]
proposed
a
dataset
of
sheep
f
acial
images
and
a
frame
w
ork
that
le
v
erages
TL
and
ne-tuning
to
automatically
dif
ferentiate
between
images
of
sheep
f
aces
sho
wing
pain
and
those
that
appear
normal.
In
addition,
T
ammina
[
1
3]
emplo
yed
the
V
GG-16
model,
which
is
a
pre-trained
deep
CNN,
to
classify
images.
This
study
presents
an
inno
v
ati
v
e
methodology
for
the
identication
of
horse
health
status
via
the
use
of
DL
methodologies.
This
study
introduces
se
v
eral
k
e
y
inno
v
ations
in
horse
health
monitoring:
i)
HorseSet
1.0:
a
comprehensi
v
e
dataset
of
horse
wellness
images,
capturing
di
v
erse
situations,
weather
conditions,
lighting,
and
angles.
ii)
Expert-v
alidated
data:
annotated
and
re
vie
wed
by
v
eterinary
specialists
with
o
v
er
a
decade
of
e
xperience,
ensuring
reliability
and
accurac
y
.
iii)
HorseNet:
an
ef
cient
DL
approach
for
rapid
and
accurate
detection
and
classication
of
horse
wellness.
i
v)
Performance
e
v
aluation:
implementation
and
assessment
of
multiple
DL
algori
thms
(V
GG16,
Incep-
tionV3,
proposed
CNN)
using
the
no
v
el
dataset.
This
w
ork
is
structured
in
the
follo
wing
manner:
section
2
pro
vided
a
thorough
e
xamination
of
the
current
body
of
research
pertaining
to
the
use
of
DL
algorithms
in
horse
monitoring.
Section
3
of
the
document
pro
vides
a
comprehensi
v
e
e
xposition
of
the
proposed
model,
including
a
thorough
depiction
of
the
dataset
and
the
DL
methodologies
used
within
it.
Section
4
presents
the
results
of
the
study
and
ho
w
the
y
were
found.
It
includes
an
y
statistical
analyses
or
pictures
of
the
data,
lik
e
the
F1-score,
accurac
y
,
confusion
matrix,
loss,
precision,
recall,
and
loss.
Section
5
encompasses
an
analysis
and
interpretation
of
the
obtained
results.
This
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
32,
No.
1,
April
2025:
555–568
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
557
includes
a
comprehensi
v
e
discussion
on
the
implications
of
the
ndings
for
the
proposed
model
as
well
as
a
thorough
performance
study
of
the
model’
s
outcomes.
Additionally
,
this
part
pro
vides
a
concise
comparison
of
the
DL
technologies
included
in
the
proposed
model.
The
last
part
presents
a
concise
o
v
ervie
w
of
the
primary
disco
v
eries
and
ramications
of
the
in
v
estig
ation.
Furthermore,
this
part
pro
vides
an
analysis
of
potential
a
v
enues
for
further
study
.
2.
LITERA
TURE
REVIEW
AND
THEORETICAL
FRAMEW
ORK
2.1.
Literatur
e
r
e
view
The
application
of
ML
and
DL
in
animal
health
monitoring
has
g
ained
momentum,
with
studies
focus-
ing
on
v
arious
species,
including
horses.
F
or
instance,
P
allathadka
et
al.
[14]
demonstrated
the
ef
fecti
v
eness
of
DL
in
identifying
lameness
in
horses
through
video
analysis,
achie
ving
a
91%
accurac
y
,
outperforming
tradi-
tional
methods.
DL
has
also
been
applied
to
detect
colic
in
horses
using
data
from
wearable
sensors,
sho
wing
90%
precision
[15],
and
has
pro
vided
insights
into
horse
beha
vior
,
tness,
and
athletic
abilities
[16].
In
pig
health
monitoring,
Ka
vlak
et
al.
[17]
emplo
yed
ML
(e
xtreme
gradient
boosting
(XG-boost))
to
predict
health
issues
by
analyzing
feeding
beha
viors.
Although
the
approach
sho
wed
good
sensiti
vity
and
specicity
,
it
f
aced
challenges
due
to
unbalanced
data
and
lo
w
symptom
pre
v
alence,
emphasizing
the
need
for
better
data
quality
and
swine
management
strate
gies
[17].
Similarly
,
TL,
which
a
dapts
pre-trained
models
for
ne
w
ta
sks,
has
been
e
xplored
for
wildlife
monitoring
by
Nguyen
et
al.
[18].
Their
DL-based
system
sho
wed
promise
in
identifying
animals
in
images,
e
v
en
with
imbalanced
datasets.
The
study
suggests
further
enhancements
through
augmented
datasets
and
adv
anced
CNN
models.
Thermal
image
processing
combined
with
ML
has
been
used
to
detect
a
vian
diseases.
Sade
ghi
et
al.
[19]
achie
v
ed
high
accurac
y
using
support
v
ector
machines
(SVM)
and
articial
neural
netw
orks
(ANN)
to
classify
diseases
lik
e
ne
wcastle
disease
and
a
vian
inuenza
in
broilers.
After
optimization,
the
SVM
with
Dempster–Shafer
e
vidence
theory
outperformed
ANN,
with
o
v
er
97%
accurac
y
in
disease
classication
[19].
Another
study
by
Quaderi
et
al.
[20]
applied
DL
to
detect
beehi
v
e
sounds
on
their
o
wn
datasets
and
using
v
arious
methods
to
reduce
features.
Sequenti
al
neural
netw
orks
with
AdaMax
and
sigmoid
acti
v
ation
functions
performed
well,
outperforming
other
methods.
Random
forests
were
also
ef
fecti
v
e.
When
combining
dif
ferent
types
of
data,
sequential
neural
netw
orks
ag
ain
sho
wed
the
best
results.
Recurrent
neural
netw
orks
were
particularly
good
at
distinguishing
bee
sounds
from
noise
[20].
DL
has
also
been
inte
grated
with
internet
of
things
(IoT)
technology
for
animal
care
and
surv
eillance.
P
atil
and
Ansari
[21]
de
v
eloped
an
intelligent
system
using
CNNs
and
recurrent
neural
netw
orks
(RNNs)
to
monitor
animal
health,
recognize
acti
vities,
and
detect
en
vironmental
anomalies.
The
study
highlights
t
h
e
potential
of
DL
to
impro
v
e
animal
welf
are
through
proacti
v
e
care.
Sreede
vi
and
Anitha
[22]
focused
on
using
DL
for
wildlife
detection
vi
a
images
and
videos,
sho
wing
that
CNN-based
approaches
are
ef
fecti
v
e
for
identifying
dif
ferent
wild
animal
species,
with
practical
implications
for
wildlife
conserv
ation.
The
welf
are
of
animals
has
become
a
critical
concern,
leading
to
the
de
v
elopment
of
wearable
health
monitoring
de
vices
using
IoT
.
These
de
vices
collect
vital
signs
such
as
body
temperature,
heart
rate,
and
respiration
rate,
which
can
be
transmitted
to
v
eterinary
professionals
for
timely
interv
ention.
The
cattle
industry
,
in
particular
,
could
benet
from
such
systems,
which
allo
w
continuous
mon-
itoring
of
indi
vidual
animals’
welf
are.
A
study
described
a
telemonitoring
system
prototype
using
wearable
technology
to
enhance
decision-making
and
impro
v
e
horse
welf
are.
Digital
tools
ha
v
e
the
potential
to
enhance
equine
health
monitoring
by
impro
ving
the
accurac
y
and
ef
cienc
y
of
health
assessments
[23].
While
there
ha
v
e
been
signicant
adv
ances
in
using
DL
for
equine
heal
th
monitoring,
there
are
still
areas
that
need
impro
v
ement.
Research
has
primarily
focused
on
detecting
lameness
and
colic
in
horses,
with
limited
studies
e
xploring
respi-
ratory
and
metabolic
issues.
DL
requires
lar
ge,
high-quality
datasets,
making
data
acquisition
and
standardiza-
tion
challenging.
Additionally
,
user
-friendly
tools
are
needed
to
inte
grate
DL
into
health
monitoring
practices.
Despite
these
challenges,
DL
of
fers
promising
opportunities
to
enhance
horse
welf
are,
performance,
and
eco-
nomic
v
alue.
Future
research
should
address
these
g
aps
and
de
v
elop
practical
tools
for
equine
health
monitoring
[24].
T
o
g
ain
a
comprehensi
v
e
understanding
of
the
e
xisting
approaches
for
horse
health
classication,
we
con-
ducted
a
detailed
o
v
ervie
w
of
the
v
arious
methodologies
emplo
yed
in
prior
studies
as
illustrated
in
T
able
1.
This
re
vie
w
pro
vides
an
o
v
ervie
w
of
v
arious
methodologies
used
in
horse
health
cl
assication,
highlighting
dataset
characteristics,
classication
algorithms,
and
performance
metrics.
The
analysis
serv
es
as
a
v
alu-
able
resource
for
researchers
and
identies
g
aps
and
opportunities
for
further
impro
v
ement
in
horse
health
monitoring.
Hor
seNet:
a
no
vel
deep
learning
appr
oac
h
for
hor
se
health
classication
(Nesrine
Atitallah)
Evaluation Warning : The document was created with Spire.PDF for Python.
558
❒
ISSN:
2502-4752
T
able
1.
Comparison
of
related
w
orks
Reference
Y
ear
Domain
Algorithm
Problem
to
Performance
No.
Used
be
solv
ed
[14]
2023
Identication
of
DL
Lameness
identication
Accurac
y
of
91%
horse
lameness
of
horse
[15]
2023
Analysis
from
wearable
DL
Detect
of
Precision
90%
sensors
to
detect
colic
the
CO
VID-19
virus
issue
in
horse
from
wearable
sensors
[16]
2023
The
heal
th
and
CNN
Monitoring
the
horses
DeepLabCut
DLC
tness
of
horses
using
g
ait
analysis
V
er2.2
tool
used
[17]
2023
Enhanced
da
ta
XGBoost
Data
quality
Animal
diseases
quality
enhancement
management
[18]
2017
Automated
wi
ldlife
CNN
Detection
and
identication
W
ildlife
spotter
monitoring
TL
of
the
classied
species
image
dataset,
species
accurac
y
of
96.6%
[19]
2023
Classication
of
SVM
with
Ef
cac
y
of
thermograph
y
and
Accurac
y
of
o
v
er
ne
wcastle
disease
Dempster–Shafer
ML
techniques
in
the
97%
e
vidence
theory
classication
of
ne
wcastle
disease
and
a
vian
inuenza
among
broilers
[20]
2022
Beehi
v
e
sound
CNN,
RNN
Classify
bee
sounds
from
AdaMax
optimizer
,
analysis
the
non-beehi
v
e
noises
accurac
y
of
85%
[21]
2020
Smart
surv
eillance
CNN,
RNN
Monitor
stray
dog
Accurac
y
of
85-90
%
of
stray
animals
dog
animals
in
a
particular
area
[22]
2022
W
ild
animal
CNN,
rectied
W
ildlife
animal
detection
Accurac
y
of
87.8%
classication
linear
unit
(ReLU)
IW
ildCam
dataset
[24]
2023
Inte
grat
ion
of
DL
Y
OLO
v7
A
h
ybrid
technique
of
Accurac
y
of
96.2%
monitoring
for
horse
automated
muzzle
feature
health
surv
eillance
e
xtraction
empo
wered
by
Y
olo
and
SIFT
,
and
feature
matching
using
FLANN
2.2.
T
ransfer
lear
ning
con
v
olutional
neural
netw
orks
TL
in
v
olv
es
le
v
eraging
a
model
pre-trained
on
one
problem
to
solv
e
a
dif
ferent
problem.
This
ap-
proach
is
g
aining
traction
in
the
realm
of
deep
neural
netw
orks,
gi
v
en
their
substantial
data
and
computational
demands.
It
allo
ws
for
a
more
ef
cient
training
process
by
rening
a
pre
viously
trained
DL
model
for
a
similar
task.
Essentially
,
it
harnesses
the
insights
a
model
has
g
arnered
from
a
data-rich
task
and
applies
it
to
a
ne
w
task
with
limited
data.
T
o
circumv
ent
the
challenges
of
training
duration
and
data
v
olume,
we
emplo
yed
TL
on
tw
o
distinct
pre-trained
CNN
models:
V
GG16,
pioneered
by
Simon
yan
and
Zisserman
[25],
and
InceptionV3,
crafted
by
Google’
s
research
team.
2.2.1.
V
GG16
The
V
GG16
model,
de
v
eloped
by
the
visual
geometry
group
at
Oxford
Uni
v
ersity
,
is
a
reno
wned
neu-
ral
netw
ork
architecture
for
image
classication.
Its
simple
yet
ef
cient
design
comprises
16
layers
(13
con-
v
olutional
and
3
fully
connected),
enabling
the
e
xtraction
of
hierarchical
features
from
images.
The
model’
s
depth
allo
ws
it
to
learn
comple
x
i
mage
representations,
making
it
v
ersatile
for
v
arious
visual
recognition
tasks.
V
GG16’
s
straightforw
ard
structure
and
high
performance
ha
v
e
made
it
inuential
in
adv
ancing
DL
and
com-
puter
vision,
cementing
its
popularity
among
researchers
and
practitioners
in
the
eld
[26].
2.2.2.
InceptionV3
InceptionV3,
de
v
eloped
by
Google,
is
a
sophisticated
CNN
architecture
designed
for
lar
ge-scale
image
recognition
and
classication.
Its
inno
v
ati
v
e
desi
gn
features
inception
modules
that
capture
information
at
multiple
scales,
striking
an
optimal
balance
between
depth
and
computational
ef
cienc
y
.
This
approach
enables
high
accurac
y
in
image
classication
tasks
while
maintaining
scalability
for
lar
ge
datasets.
InceptionV3’
s
v
ersatility
has
l
ed
to
its
widespread
adoption
across
v
arious
domains,
consistently
ac
hie
ving
top-tier
results
in
benchmark
competitions.
Its
combination
of
performance
and
ef
cienc
y
mak
es
it
a
popular
choice
for
comple
x
image
recognition
tasks,
particularly
when
processing
e
xtensi
v
e
datasets
[27].
Due
to
its
impressi
v
e
performance
and
ef
cienc
y
,
t
he
InceptionV3
model
has
become
a
reference
architecture
in
the
eld
of
DL
and
image
classication.
It
has
serv
ed
as
a
foundation
for
subsequent
adv
ancements
in
CNN
architecture
and
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
32,
No.
1,
April
2025:
555–568
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
559
has
inuenced
the
de
v
elopment
of
ne
wer
models.
Researchers
and
practitioners
alik
e
continue
to
le
v
erage
the
capabilities
of
InceptionV3
to
tackle
comple
x
image
recognition
challenges
and
push
the
boundaries
of
computer
vision
applications.
3.
METHODS
AND
MA
TERIALS
3.1.
Dataset
In
order
to
ef
fecti
v
ely
classify
and
detect
the
well-being
of
horses
using
camera
and
video
foota
ge,
we
utilized
adv
anced
computer
vision
techniques
and
DL
algorithms.
This
inno
v
ati
v
e
approach
a
llo
wed
us
to
de
v
elop
a
system
capable
of
analyzing
visual
data
and
e
xtracting
meaningful
insights
related
to
t
he
health
and
condition
of
horses.
In
the
upcoming
section,
we
will
introduce
and
elaborate
on
our
meticulously
curated
dataset,
kno
wn
as
HorseNet.
This
dataset
plays
a
crucial
role
in
our
research
as
it
serv
es
as
the
foundation
for
training
and
e
v
aluating
the
performance
of
our
models.
The
subsequent
discussion
will
encompass
a
detailed
description
of
the
dataset,
including
its
composition,
size,
and
the
specic
attrib
utes
and
features
captured
within.
By
shedding
light
on
the
intricacies
of
HorseNet,
we
aim
to
pro
vide
a
comprehensi
v
e
understanding
of
the
underlying
data
that
po
wers
our
horse
wellness
classication
and
detection
system.
3.1.1.
Study
ar
ea
The
research
study
w
as
conducted
at
the
esteemed
horse
club
located
in
Ahsa,
a
re
gion
kno
wn
for
i
ts
rich
equestrian
culture.
During
the
initial
stages
of
the
research,
the
authors
encountered
a
challenge
in
nding
a
suitable
dataset
that
adequately
represented
the
di
v
erse
range
of
horses
and
conditions
rele
v
ant
to
their
study
.
T
o
o
v
ercome
this
hurdle,
the
authors
took
the
initiati
v
e
to
g
ather
and
curate
the
necessary
data
themselv
es.
The
y
meticulously
collected
a
comprehensi
v
e
set
of
images
featuring
horses,
ensuring
that
it
encompassed
a
wide
spectrum
of
breeds,
ages,
and
ph
ysical
attrib
utes.
These
images
wer
e
specically
sourced
from
the
horse
club
situated
in
the
capti
v
ating
Ahsa
re
gion
of
Saudi
Arabia,
which
boast
s
a
notable
reputation
for
its
dedication
to
the
equestrian
arts.
By
acti
v
ely
eng
aging
with
the
horse
club
and
obtaining
their
cooperation,
the
authors
were
able
to
acquire
a
di
v
erse
and
representati
v
e
dataset
that
formed
the
foundation
of
their
research.
3.1.2.
Data
collection
Our
research
endea
v
ors
led
us
to
amass
a
v
ast
and
comprehensi
v
e
collection
of
horse
images
and
videos,
encompassing
a
di
v
erse
range
of
breeds,
ages,
and
v
arying
states
of
health
conditions.
T
o
accomplish
this,
we
es
tablished
a
fruitful
partnership
with
kno
wledgeable
members
of
the
esteemed
horse
club
.
W
orking
in
collaboration,
we
utilized
smartphones
equipped
with
high-quality
cameras
to
capture
the
visual
data.
The
con
v
enience
and
portability
of
these
de
vices
allo
wed
us
to
ef
ciently
document
the
horses
in
thei
r
natural
en
vi-
ronments,
ensuring
the
authenticity
and
representati
v
es
of
the
collected
media.
Figure
1
illustrates
the
process,
sho
wcasing
the
use
of
smartphones
in
capturing
the
images
and
videos
that
constitute
our
e
xtensi
v
e
dataset.
Through
this
diligent
and
collaborati
v
e
ef
fort,
we
were
able
to
create
a
rich
and
di
v
erse
resource
that
forms
the
backbone
of
our
research
on
horse
wellness.
Figure
1.
Horse
wellness
dataset
samples
Hor
seNet:
a
no
vel
deep
learning
appr
oac
h
for
hor
se
health
classication
(Nesrine
Atitallah)
Evaluation Warning : The document was created with Spire.PDF for Python.
560
❒
ISSN:
2502-4752
The
pictures
were
stored
as
PNG
les
in
multiple
resolutions.
Notably
,
the
primary
focus
of
the
dataset
is
on
images
depicting
horses
in
di
v
erse
settings.
These
photos
were
g
athered
from
January
to
April
2023
and
were
inuenced
by
dif
ferent
weather
and
illumination
conditions.
The
y
were
also
shot
from
a
range
of
perspecti
v
es.
Out
of
the
entire
collection,
onl
y
1,218
pictures
met
the
criteria
for
the
research.
The
rest,
which
were
either
out
of
focus
or
didn’
t
portray
the
necessary
scenarios
in
v
olving
horses,
were
e
xcluded.
3.1.3.
Horse
wellness
classications
DL
techniques,
specically
CNNs,
ha
v
e
sho
wn
grea
t
potential
in
v
arious
image
recognition
tasks.
While
s
ome
aspects
of
f
atigue,
lameness,
colic,
and
anemia
in
horses
can
potentially
be
detected
usi
n
g
images
and
DL.
In
the
follo
wing,
there
are
some
aspects
that
might
be
detected
using
images
and
DL:
i)
Posture
and
g
ait
analysis:
DL
algorithms
can
analyze
images
or
videos
of
a
horse’
s
posture
and
g
ait
to
detect
abnormalities,
such
as
limping,
sti
f
fness,
or
une
v
en
weight
distrib
ution.
This
analysis
can
help
classify
the
se
v
erity
and
type
of
lameness.
ii)
Ph
ysical
appear
ance:
f
atigue,
anemia,
and
colic
can
cause
changes
in
a
horse’
s
ph
ysical
appearance
that
may
be
detectable
using
DL.
F
or
e
xample,
an
anemic
horse
may
e
xhibit
pale
mucous
membranes
(gums),
and
a
horse
with
colic
may
sho
w
signs
of
discomfort
or
abdominal
distension.
DL
models
can
be
trained
to
recognize
these
features
from
images
and
identify
horses
that
may
need
further
e
xamination.
iii)
Beha
vioral
patterns:
horses
suf
fering
from
f
atigue,
lameness,
colic,
or
anemia
may
e
xhibit
abnormal
be-
ha
viors
such
as
restlessness,
rolling,
or
frequent
changes
in
position.
By
analyzing
a
series
of
images
or
videos,
DL
models
may
be
able
to
detect
these
beha
vior
patterns
and
help
in
identifying
af
fected
horses.
T
able
2
presents
a
comprehensi
v
e
cate
gorization
of
horse
wellness,
outlining
six
distinct
classes
of
horse
be-
ha
vior
and
health.
It
ranges
from
the
”Normal
Horse”
cate
gory
,
which
comprises
the
majority
with
178
horses,
to
more
specic
beha
viors
lik
e
”Rolling
Horse”
and
”Stretching
Horse”.
T
able
2.
Horse
wellness
cate
gories
W
ellness
class
Class
name
Number
Normal
horse
0
478
Rolling
horse
1
259
Stretching
horse
2
21
P
a
wing
3
358
L
ying
do
wn
4
38
Biting
at
sides
5
62
Kicking
belly
6
2
3.1.4.
Data
distrib
ution
In
T
able
3,
we
present
a
comprehensi
v
e
o
v
ervie
w
of
the
horse
wellness
datasets
used
in
our
study
.
Upon
analyzing
these
datasets,
we
observ
ed
an
inherent
imbalance
in
the
distrib
ution
of
instances
across
the
v
arious
horse
wellness
cate
gories.
This
imbalance
posed
a
challenge
as
it
could
potentially
bias
the
performance
of
our
DL
algorithms
during
training.
T
o
address
this
issue
and
promote
f
airness
in
our
model
training,
we
em-
plo
yed
image
enhancement
methods.
These
techniques
allo
wed
us
to
manipulate
and
augment
the
dataset,
ensuring
a
more
balanced
distr
ib
ution
of
instances
across
the
dif
ferent
horse
wellness
cate
gories.
By
equal-
izing
the
representation
of
each
cate
gory
,
we
aimed
to
enhance
the
algorithm’
s
ability
to
learn
and
generalize
patterns
associated
with
the
full
spectrum
of
horse
wellness.
Through
the
application
of
these
image
enhance-
ment
methods,
we
aimed
to
mitig
ate
an
y
potential
biases
that
could
arise
due
to
the
imbalanced
nature
of
the
original
dataset,
ultimately
fostering
more
accurate
and
reliable
results.
T
able
3.
Distrib
ution
in
our
dataset
No.
Class
name
Number
of
samples
1
Normal
horse
478
2
Abnormal
horse
740
T
otal
1,218
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
32,
No.
1,
April
2025:
555–568
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
561
3.2.
Methods
The
proposed
detection
and
classication
frame
w
ork
entails
a
well-dened
w
orko
w
comprising
v
e
k
e
y
steps:
g
athering
data,
rening,
and
augmenting
this
data,
emplo
ying
v
arious
CNN
models,
and
conducting
e
xperiments
and
assessments
as
depicted
in
Figure
2.
These
steps
collecti
v
ely
contrib
ute
to
the
accurac
y
and
ef
fecti
v
eness
of
the
frame
w
ork.
Figure
2.
The
e
xperimentation
w
orko
w
3.2.1.
Data
pr
e-pr
ocessing
A
critical
phase
in
an
y
computer
vision
(CV)
system
in
v
olv
es
prepossessing
the
images.
Initi
ally
,
e
v
ery
image
w
as
resized
to
dimensions
of
300×300
to
ensure
square-shaped
images
and
consistenc
y
throughout
the
dataset.
Subsequently
,
these
images
were
altered
to
match
the
input
dimensions
of
v
arious
models.
F
or
the
custom
designed
HorseNet,
the
images
were
adjusted
to
256×256
pix
els.
F
or
V
GG16
and
traditional
CNN
models,
the
y
were
changed
to
224×224
pix
els.
Meanwhile,
the
input
for
InceptionV3
w
as
resized
to
71×71
pix
els.
3.2.2.
Data
augmentation
DL
models
typicall
y
need
a
v
ast
am
o
unt
of
data
for
opti
mal
performance.
When
there’
s
limited
t
rain-
ing
data
a
v
ailable,
image
augmentation
is
often
used
to
bolster
the
rob
ustness
o
f
image
classiers.
Image
augmentation
articially
produces
training
samples
using
techniques
lik
e
rotation,
noise
addition,
shifting,
mir
-
roring,
and
blurring.
From
the
initial
dataset
of
1,218
images,
after
augmentation,
it
w
as
split:
90%
w
as
used
for
training
and
v
alidation,
and
10%
for
testing,
as
illustrated
in
T
able
4.
In
this
study
,
we
emplo
yed
four
augmentation
methods:
rotation,
ipping,
zoom,
and
brightness
adjustments.
Rotating
images
is
a
common
technique,
e
xpanding
the
dataset
by
producing
v
ariants
of
the
original
images
rotating
an
ywhere
from
0
to
360
de
grees.
Flipping,
on
the
other
hand,
can
be
seen
as
a
subset
of
rotation,
creating
mirrored
v
ersions
of
the
original.
T
able
4.
Distrib
ution
of
images
dataset
after
data
augmentation
Normal
Abnormal
T
otal
T
raining
data
6,920
9,740
16,658
V
alidation
data
2,488
2,170
4,658
T
esting
data
1,485
1,476
2,961
T
otal
10,893
13,386
24,277
Hor
seNet:
a
no
vel
deep
learning
appr
oac
h
for
hor
se
health
classication
(Nesrine
Atitallah)
Evaluation Warning : The document was created with Spire.PDF for Python.
562
❒
ISSN:
2502-4752
3.3.
Model
design
3.3.1.
Pr
oposed
CNN
This
subsection
tackled
the
architecture
of
the
proposed
traditional
CNN
designed
for
the
clas
sica-
tion
of
horse
wellness.
The
model
is
composed
of
six
main
components:
10
con
v
olutional
layers
paired
with
4
pooling
layers,
tw
o
fully
connected
layers,
and
a
ReLU
acti
v
ation
layer
.
Dif
ferent
lters
were
utilized
in
each
con
v
olution
layer
to
e
xtract
v
aried
features
[28].
T
o
counteract
o
v
er
tting,
dropout
w
as
inte
grated
as
a
re
gularization
technique
within
both
the
max-pooling
and
the
fully
connected
layers.
The
input
images
had
dimensions
of
32×32×3.
By
choosing
a
batch
size
of
64
and
a
learning
rate
of
0.0001,
the
training
speed
w
as
optimized.
T
w
o
deep
layers
were
established:
the
inaugural
layer
took
an
input
channel
of
one,
featuring
a
3×3
k
ernel,
a
stride
of
one,
and
a
padding
v
alue
of
tw
o.
Post-con
v
olution,
the
image
dimensions
shrank;
ho
w-
e
v
er
,
padding
w
as
adjusted
to
zero
to
retain
the
original
size.
The
ReLU
function
w
as
f
a
v
ored
as
the
model’
s
acti
v
ation
due
to
its
resilience
ag
ainst
saturation
and
its
gradient
performance
relati
v
e
to
other
acti
v
ation.
The
max-pooling
layer
emplo
yed
both
a
k
ernel
and
stride
of
tw
o.
F
or
the
subsequent
con
v
olutional
layer
,
the
input
dimension
w
as
32,
output
w
as
64,
using
a
5x5
k
ernel,
a
stride
of
one,
and
padding
of
tw
o.
The
ReLU
and
max
pooling
remained
consistent
across
both
layers.
A
dropout
layer
w
as
inte
grated
to
alle
viate
and
mitig
ate
o
v
er
tting.
Conclusi
v
ely
,
tw
o
fully
connected
layers
ensured
the
interlinking
of
all
neurons.
The
CNN’
s
blueprint
can
be
visualized
in
Figure
3.
Figure
3.
Architecture
of
the
proposed
CNN
3.3.2.
Pr
oposed
appr
oach
In
this
research,
we
utilized
three
distinct
models:
a
custom-b
uilt
CNN,
V
GG-16,
and
InceptionV3.
W
e
trained
our
models
using
augmented
data
from
the
primary
dataset,
as
detailed
in
section
3.1..
Post-
augmentation,
the
e
ntire
dataset,
consisting
of
24,277
images,
w
as
split
into
16,658
training,
4,658
v
alidation,
and
2,961
test
images,
as
illustrated
in
T
able
4.
W
e
adopted
TL
method
to
address
the
challenges
of
e
xtended
training
times
and
limited
data
a
v
ailabil
ity
.
Moreo
v
er
,
the
top
layers,
along
with
the
gully
connected
(FC)
layers
added
to
the
tail-end
of
the
pre-e
xisting
models,
were
set
to
a
static
state,
and
subsequently
retrained
on
our
specic
dataset
to
achie
v
e
the
tar
geted
outcomes.
In
our
methodology
,
we
tha
wed
the
concluding
layers
of
these
established
models,
then
retrained
them
using
our
dataset,
while
k
eeping
the
preliminary
l
ayers
frozen.
This
process
is
visualized
in
Figure
4.
4.
RESUL
TS
AND
DISCUSSION
In
this
section,
we
will
pro
vide
a
comprehensi
v
e
summary
of
the
results
obtained
from
the
e
xperi-
ments
conducted
o
n
the
dataset
pertaining
to
horse
well-being.
These
e
xperiments
were
conducted
with
the
aim
of
utilizing
adv
anced
CV
techniques
and
DL
algorithms
to
g
ain
insights
into
the
health
and
condition
of
horses.
The
collected
dataset,
comprising
a
di
v
erse
collection
of
horse
images
and
videos,
formed
the
basis
for
the
e
xperiments.
Using
this
dataset,
we
trained
and
e
v
aluated
se
v
eral
CNN
models
specically
designed
for
horse
well-being
analysis.
These
models
were
selected
based
on
their
established
performance
in
image
classication
tasks
and
their
suitability
for
the
domain
of
horse
well-being.
Through
rigorous
e
xperimentation
and
analysis,
we
obtained
v
aluable
ndings
re
g
arding
the
classication
and
detection
of
horse
well-being
using
Dl
techniques.
The
results
shed
light
on
the
ef
cac
y
of
the
emplo
yed
CNN
models
in
accurately
assessing
v
ar
-
ious
aspects
of
horse
well-being,
such
as
o
v
erall
health,
body
condition,
and
an
y
potential
signs
of
discomfort
or
distress.
Furthermore,
in
order
to
pro
vide
a
comprehensi
v
e
analysis,
we
compared
the
performance
of
the
dif
ferent
CNN
models
used
in
the
e
xperiments.
This
comparati
v
e
analysis
allo
wed
us
to
identify
the
strengths
and
weaknesses
of
each
model,
enabling
us
to
mak
e
informed
decisions
re
g
arding
their
suitability
for
specic
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
32,
No.
1,
April
2025:
555–568
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
563
horse
well-being
assessment
tasks.
By
presenting
these
summarized
results
and
conducting
a
thorough
analy-
sis,
we
aim
to
contrib
ute
to
the
e
xisting
body
of
kno
wledge
utilizing
CV
and
DL
for
horse
well-being
analysis.
The
ndings
obtained
from
these
e
xperiments
ha
v
e
the
potential
to
adv
ance
the
understanding
of
horse
health
assessment,
potentially
leading
to
impro
v
ed
care
and
well-being
for
these
magnicent
animals.
Figure
4.
Proposed
approach
for
horse
wellness
detection
and
binary-classication
4.1.
Experimental
setup
In
our
research,
we
emplo
yed
the
Jupiter
notebook
to
script
the
entire
w
orko
w
using
Python
3.8.
T
o
b
uild
the
neural
netw
orks,
we
incorporated
both
the
K
eras
library
and
T
ensorFlo
w
as
the
back
end.
Furthermore,
OpenCV
f
acilitated
data
loading
and
pre-processing,
while
S
ci-Kit
Learn
w
as
instrumental
in
generating
classi-
cation
sum
maries.
F
or
e
xpedited
computational
performance,
we
utilized
the
Nvidia
GeF
orce
MX
250
GPU,
supplemented
by
CUD
A
and
cuDNN
libraries.
cuDNN
is
a
GPU-enhanced
library
tailored
to
boost
v
arious
DL
frame
w
orks.
The
system
underpinning
our
w
ork
had
these
specications:
a
64-bit
processor
,
an
Intel
Core
i7-8565U
CPU
clocking
at
1.80
GHz,
and
16
GB
of
RAM.
It
operated
on
a
W
indo
ws
10
platform
equipped
with
NVIDIA
GeF
orce
MX.
4.2.
Experimental
r
esults
The
main
objecti
v
e
of
the
suggested
approach
is
to
accurately
identify
and
cate
gorize
the
health
of
horses.
T
o
achie
v
e
this,
we
utilized
three
distinct
CNN
models.
T
able
5
presents
the
performance
metrics
of
the
CNN
models
post
ne-tuning
and
t
raining
on
the
horse
wellness
dataset.
Among
the
models,
InceptionV3
stands
out
with
commendable
accurac
y
,
precision,
and
recall
rate,
each
at
97%.
This
is
trailed
by
V
GG16
and
subsequently
the
t
raditional
CNN.
Ov
erall,
the
models
grounded
in
TL
sho
wcased
rob
ust
performance,
boast-
ing
an
accurac
y
surpassing
95%.
InceptionV3
emer
ges
as
the
top
performer
with
all
metrics
at
or
near
97%.
This
indicates
not
only
high
accurac
y
b
ut
also
a
balanced
capability
in
precision
and
recall,
suggesting
a
fe
w
f
alse
positi
v
es
and
f
alse
ne
g
ati
v
es.
This
balance
is
crucial
in
medical
or
wellness
conte
xts
where
both
types
of
errors
carry
signicant
consequences.
V
GG16
follo
ws
closely
,
demonstrating
that
while
slightly
less
ef
fecti
v
e
than
InceptionV3,
it
still
pro
vides
a
highly
reliable
met
hod
for
classifying
horse
health,
with
metrics
around
95%.
Proposed
CNN,
while
trailing
behind
the
other
tw
o
models,
still
sho
ws
respectable
performance
metrics
(90%).
This
indicates
a
viable
option
when
computational
resources
are
limited
or
for
preliminary
e
xplorations.
Hor
seNet:
a
no
vel
deep
learning
appr
oac
h
for
hor
se
health
classication
(Nesrine
Atitallah)
Evaluation Warning : The document was created with Spire.PDF for Python.
564
❒
ISSN:
2502-4752
T
able
5.
Performance
metrics
of
the
deplo
yed
TL-based
models
and
the
proposed
CNN
Model
Accurac
y
Precision
Recall
F1
score
Proposed
CNN
90.20%
90%
90%
90%
V
GG16
96.18%
96%
96%
96%
InceptionV3
96.92%
97%
97%
97%
Figure
5
sho
ws
the
plots
of
confusion
matrices
for
each
horse
wellness
class
produced
by
the
pro-
posed
CNN,
V
GG16,
and
InceptionV3
models.
Figure
5(a)
represents
the
model
with
the
lo
west
performance,
demonstrating
greater
dif
culty
in
distinguishing
between
normal
and
abnormal
conditions
compared
to
the
others.
Figure
5(b)
sho
ws
mark
ed
impro
v
ement
in
both
sensiti
vity
and
specicity
,
with
a
balanced
decrease
in
both
f
alse
positi
v
es
and
f
alse
ne
g
ati
v
es,
suggesting
better
o
v
erall
performance.
Figure
5(c)
stands
out
as
the
best
performer
,
with
the
highest
true
positi
v
e
and
true
ne
g
ati
v
e
rates,
coupled
with
the
lo
west
f
alse
positi
v
e
and
f
alse
ne
g
ati
v
e
rates,
indicating
a
highly
accurate
model.
The
progression
observ
ed
is
consistent
with
the
general
e
xpectation
that
more
adv
anced
models
with
more
parameters
and
sophisticated
ar
chitectures,
often
utilizing
TL,
will
generally
outperform
simpler
models,
particularly
on
comple
x
tasks
lik
e
image
classication.
(a)
(b)
(c)
Figure
5.
The
confusion
matrix
of:
(a)
the
proposed
CNN
model,
(b)
the
pre-trained
V
GG16,
and
(c)
the
pre-trained
InceptionV3
approach
The
analysis
of
curv
es
depicted
in
Figures
6,
which
includes
the
smoothed
training
curv
es
and
v
alida-
tion
loss
and
accurac
y
curv
es
of
the
proposed
CNN
model
in
Figure
6(a),
the
ore-trained
V
GG16
approach
in
Figure
6(b),
and
the
pre-trained
InceptionV3
approach
in
Figure
6(c).
This
Figure
suggests
that
the
pre-trained
InceptionV3
model
is
the
best
performer
in
terms
of
learning
ef
fecti
v
ely
and
general
izing
from
the
training
data
to
the
v
alidation
data.
It
also
manages
to
achie
v
e
high
accurac
y
while
a
v
oiding
o
v
er
-tting,
making
it
the
most
suitable
model
for
deplo
yment
in
real-w
orld
scenarios
where
model
rob
ustness
and
reliability
are
crucial.
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
32,
No.
1,
April
2025:
555–568
Evaluation Warning : The document was created with Spire.PDF for Python.