Indonesian
J
our
nal
of
Electrical
Engineering
and
Computer
Science
V
ol.
40,
No.
1,
October
2025,
pp.
202
∼
215
ISSN:
2502-4752,
DOI:
10.11591/ijeecs.v40.i1.pp202-215
❒
202
DigiScope:
IoT
-enhanced
deep
lear
ning
f
or
skin
cancer
pr
ognosis
A
ymane
Edder
1
,
F
atima-Ezzahraa
Ben-Bouazza
1,2,3
,
Oumaima
Manchadi
1
,
Idriss
T
afala
1
,
Bassma
Jioudi
1
1
BRET
Lab,
Mohammed
VI
Uni
v
ersity
of
Sciences
and
Health,
Casablanca,
Morocco
2
LaMSN,
La
Maison
des
Sciences
Num
´
eriques,
France
3
Arfticial
Intelligence
Research
and
Application
Laboratory
(AIRA
Lab),
F
aculty
of
Science
and
T
echnology
,
Hassan
1st
Uni
v
ersity
,
Settat,
Morocco
Article
Inf
o
Article
history:
Recei
v
ed
Jun
5,
2024
Re
vised
Apr
24,
2025
Accepted
Jul
3,
2025
K
eyw
ords:
Deep
learning
Early
detection
Internet
of
things
Lo
w-cost
de
vices
Rural
areas
Skin
cancer
ABSTRA
CT
In
dermatology
,
early
identication
and
interv
ention
are
crucial
for
optimiz-
ing
patient
outcomes
in
skin
cancer
care.
Recent
technological
adv
ances,
particularly
in
the
int
ernet
of
things
(IoT),
ha
v
e
led
to
signicant
gro
wth
in
telemedicine.
This
study
introduces
a
cutting-edge
system
that
proacti
v
ely
pre-
dicts
the
emer
gence
of
skin
cancer
by
combining
deep
learning
algorithms,
IoT
de
vices,
and
sophisticated
medical
imaging
techniques.
The
e
xperimen-
tal
setup
le
v
erages
a
high-resolution
mobile
came
ra
for
dermoscop
y
,
associated
with
a
cloud-inte
grated
machine
learning
fr
ame
w
ork.
The
proposed
algorithm
comprehensi
v
ely
e
xamines
lesion
characteristics,
Utilizi
ng
color
,
te
xture,
and
shape
characteristics
to
e
v
aluate
the
probability
of
malignanc
y
.
Subsequently
,
a
cloud-hosted
m
achine
learning
model
analyzes
and
scrutinizes
the
collected
data,
yielding
a
thorough
diagnostic
e
v
aluation.
Initial
results
re
v
eal
that
this
system
achie
v
es
an
impressi
v
e
predicti
v
e
accurac
y
rate
e
xce
eding
97.6%,
en-
abling
swift
and
ef
cient
skin
cancer
detec
tion.
These
promising
ndings
em-
phasize
the
potential
for
rapid,
ef
cient,
and
proacti
v
e
diagnosis,
signicantly
impro
ving
patient
prognosis
and
reinforcing
the
v
alue
of
telemedicine
in
con-
temporary
healthcare.
This
is
an
open
access
article
under
the
CC
BY
-SA
license
.
Corresponding
A
uthor:
A
ymane
Edder
BRET
Lab,
Mohammed
VI
Uni
v
ersity
of
Sciences
and
Health
Casablanca,
Morocco
Email:
aedder@um6ss.ma
1.
INTR
ODUCTION
Skin
cancer
poses
a
signicant
health
challenge
across
the
globe,
emphasizing
the
need
for
ef
fecti
v
e
detection
and
treatment
methods
to
impro
v
e
patient
outcomes
and
reduce
its
impac
t.
The
rise
of
technology
in
healthcare
of
fers
ne
w
possibilities
for
tackling
this
challenge,
as
articial
intelligence
(AI)
and
the
internet
of
things
(IoT)
are
becoming
po
werful
tools
in
the
ght
ag
ainst
skin
cancer
.
Ben-Bouazza
et
al.
[1]
by
le
v
eraging
these
technologies,
we
are
entering
an
era
where
early
detection
and
precise
diagnosis
are
increasingly
within
reach.
Azeroual
et
al.
[2]
AI
algorithms
are
capable
of
analyzing
enormous
v
olumes
of
data,
un
v
eiling
intri-
cate
patterns
often
missed
by
human
clinicians.
This
capabilit
y
is
further
enhanced
by
the
inte
gration
of
data
streams
from
wearable
sensors
and
medical
imaging
de
vices
within
the
IoT
frame
w
ork,
enabling
comprehen-
si
v
e
analyses.
Recent
studies
underscore
the
transformati
v
e
po
wer
of
these
technologies,
Hoang
et
al.
[3]
with
deep
learning
algorithms
redening
lesion
detection
and
classication.
IoT
-enabled
de
vices,
such
as
intelligent
J
ournal
homepage:
http://ijeecs.iaescor
e
.com
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
203
skin
patches,
f
acilitate
adv
anced
data
acquisition
and
transmission
for
in-depth
analysis,
thereby
pushing
the
boundaries
of
medical
inno
v
ation.
Gajera
et
al.
[4]
emplo
yed
deep
features
deri
v
ed
from
pre-trained
con
v
olutional
neural
netw
ork
(CNN)
models
to
assess
dermoscopic
images
for
melanoma
diagnosis,
adv
ocating
for
border
localization
to
safe
guard
critical
skin
lesion
sites.
A
total
of
eight
CNN
models
were
systematically
e
xamined
for
the
purpose
of
feature
e
xtraction,
utilizing
four
distinct
datasets
in
the
e
xperimental
procedures.
The
inte
gration
of
DenseNet-121
with
a
multilayer
perceptron
yielded
a
commendable
classication
rate.
K
umar
et
al.
[5]
ef
fecti
v
ely
discerned
preliminary
indicators
of
three
distinct
types
of
skin
cancer
through
the
application
of
computational
method-
ologies.
The
y
emplo
yed
a
deep
e
v
olutionary
articial
neural
netw
ork
(DEANN)
for
the
classication
of
skin
cancer
,
alongside
techniques
such
as
local
binary
patterns
(LBP),
gray
le
v
el
co-occurrence
matrix
(GLCM),
color
space
analysis,
and
RGB
techniques
to
e
xtract
pertinent
image
features
critical
for
the
accurate
classi-
cation
of
the
condition.
Chaturv
edi
et
al.
[6]
proposed
a
methodology
for
t
he
classication
of
Malignant
Cutaneous
Melanoma
that
demonstrates
superior
performance
compared
to
both
dermatological
assessments
and
e
xisting
deep
learning
approaches.
Khan
et
al.
[7]
de
v
eloped
a
system
that
inte
grates
deep
learning
models,
specically
le
v
eraging
DenseNet
for
classication
purposes
and
mask
re
gional
con
v
olutional
neural
netw
ork
(Mask-RCNN)
for
se
gmentation
tasks.
Srini
v
asu
et
al.
[8]
utilized
MobileNet
V2
as
the
selected
architecture
for
the
cl
assication
of
di
v
erse
dermatological
conditions,
inte
grating
long
short-term
memory
(LSTM)
to
en-
hance
the
model’
s
performance
.
Hosn
y
et
al.
[9]
presented
a
no
v
el
methodology
for
the
classication
of
skin
lesions,
utilizing
transfer
l
earning
in
conjunction
with
a
deep
neural
netw
ork
architecture
kno
wn
as
Ale
xNet.
The
public
database
ISIC
2018
serv
ed
as
the
foundational
dataset
for
the
training,
testing,
and
comparati
v
e
anal-
ysis
of
the
proposed
methodology
ag
ainst
state-of-the-art
techniques.
The
methodology
ef
fecti
v
ely
classies
se
v
en
unique
cate
gories
of
skin
lesions,
with
the
authors
reporting
outs
tanding
outcomes
in
classication
per
-
formance.
Sae-Lim
et
al.
[10]
emplo
yed
a
modied
MobileNet
architecture
for
the
classication
of
skin
lesions.
The
nd
i
ngs
indicated
that
the
modied
model
e
xhibited
superior
performance
compared
to
the
con
v
entional
MobileNet
model,
as
e
videnced
by
enhancements
in
accurac
y
,
specicity
,
sensiti
vity
,
and
F1-score.
During
the
preprocessing
phase,
the
implem
entation
of
data
upsampling
and
data
augmentation
techniques
pro
v
ed
bene-
cial
in
addressing
clas
s
imbalance.
Furthermore,
data
augmentation
serv
ed
as
a
mechanis
m
to
mitig
ate
the
risk
of
o
v
ertt
ing
within
the
model.
Zaqout
et
al.
[11]
ha
v
e
formulated
an
automated
diagnostic
frame
w
ork
aimed
at
the
preliminary
e
v
aluation
of
melanoma,
utilizing
image
processing
techniques
that
are
grounded
in
the
widely
recognized
ABCD
medical
protocol.
The
proposed
system
emplo
ys
a
range
of
image
processing
techniques
to
f
acilitate
precise,
ra
pid,
cost-ef
fecti
v
e,
and
readily
accessible
diagnosis
of
melanoma.
Hasan
et
al.
[12]
introduced
an
inno
v
ati
v
e
automatic
skin
lesion
se
gmentation
netw
ork
designated
as
DSNet.
This
netw
ork
e
xhibits
rob
ustness
and
incorporates
a
proposed
loss
function
that
inte
grates
a
binary
cross-entrop
y
compo-
nent
alongside
an
intersection
o
v
er
union
component.
Ade
gun
and
V
iriri
[13]
de
v
eloped
a
deep
learning-based
computer
-aided
diagnosis
system
aimed
at
the
detection
and
identication
of
skin
lesions
for
the
purpose
of
diagnosing
skin
cancer
.
Chatterjee
et
al.
[6]
i
ntroduced
an
inno
v
ati
v
e
k
ernel
sparse
coding
methodology
aimed
at
the
se
gmentation
and
classication
of
skin
lesions.
Their
approach
demonstrated
competiti
v
e
performance
relati
v
e
to
alternati
v
e
techniques
in
e
xperimental
e
v
aluations
utilizing
both
dermoscop
y
and
digital
datasets.
S
`
aez
et
al.
emplo
yed
a
computerized
system
designed
to
quantify
melanoma
thickness
through
the
analysis
of
dermoscopic
images.
Y
u
et
al.
[14],
Hameed
et
al.
[15],
Khan
et
al.
[16],
Hoang
et
al.
[3],
Zhang
et
al.
[17],
Periera
et
al.
[18],
Shetty
et
al.
[19],
Dhi
vyaa
et
al.
[20],
Mahbod
et
al.
[21],
and
Alenezi
et
al.
[22]
ha
v
e
made
signicant
contrib
utions
to
the
domain
of
skin
lesion
classication.
Y
u
et
al.
[14]
introduced
an
inno
v
ati
v
e
methodology
for
the
classication
of
dermoscop
y
images,
emplo
ying
a
compact
architectural
frame-
w
ork
alongside
local
descriptor
encoding
techniques.
The
con
v
olutional
features
were
e
xtracted
from
an
image
emplo
ying
a
deep
residual
netw
ork,
follo
wed
by
the
application
of
the
sher
v
ector
technique
to
encode
these
features
into
more
intricate
representations.
Hameed
et
al.
[15]
emplo
yed
a
combination
of
traditional
ma-
chine
learning
methodologi
es
alongside
adv
anced
deep
learning
approaches
to
assess
early-st
age
skin
lesions.
The
deep
learning
methodology
utilized
transfer
learning
directly
from
the
images,
whereas
the
con
v
entional
approach
initially
conducted
pre-processing,
cate
gorization,
feature
e
xtraction,
and
subsequent
cate
gorization
processes.
The
proposed
methodology
demonstrated
superior
performance
compared
to
the
multi-class
single-
le
v
el
classicati
on
algorithm,
attaining
ele
v
ated
accurac
y
across
both
approaches.
Khan
et
al.
[7]
introduced
an
inno
v
ati
v
e
computer
-aided
diagnosis
(CAD)
system
aimed
at
the
classication
of
skin
lesions
through
the
application
of
deep
learning
methodologies.
The
system
emplo
ys
ResNet-50
and
ResNet-101
architectures
for
the
e
xtraction
of
features
from
enhanced
dermoscopic
images,
utilizing
a
no
v
el
methodology
referred
to
DigiScope:
IoT
-enhanced
deep
learning
for
skin
cancer
pr
o
gnosis
(Edder
A
ymane)
Evaluation Warning : The document was created with Spire.PDF for Python.
204
❒
ISSN:
2502-4752
as
KcPCA
for
the
selection
of
signicant
features.
A
multi-class
support
v
ector
machine
(SVM)
utilizing
a
radial
basis
function
k
ernel
is
emplo
yed,
incorporating
the
upper
60%
of
these
features
as
input.
Hoang
et
al.
[3]
de
v
eloped
a
straightforw
ard
methodology
for
the
classication
of
skin
lesions,
demonstrating
superior
per
-
formance
compared
to
20
alternati
v
e
methods
while
necessitating
79
times
fe
wer
parameters.
The
researchers
emplo
yed
a
deep
learning
methodology
to
ef
fecti
v
ely
se
gment
and
classify
skin
lesions,
attaining
remarkable
outcomes
when
the
lesion’
s
fore
ground
is
discernible
from
the
background
through
te
xture
and
color
dif
feren-
tiation.
Zhang
et
al.
[17]
proposed
an
inno
v
ati
v
e
methodology
utilizing
a
CNN
frame
w
ork
for
the
diagnosis
of
skin
cancer
.
A
modied
v
ariant
of
the
whale
optimization
algorithm
w
as
emplo
yed
to
enhance
the
ef
cac
y
of
CNNs
and
to
minimize
the
discrepanc
y
between
the
netw
ork’
s
output
and
the
intended
output.
Thurnhofer
-
Hemsi
et
al.
presented
an
inno
v
ati
v
e
methodology
for
the
classication
of
skin
lesions
through
the
application
of
deep
CNNs,
demonstrating
enhanced
reliability
compared
to
traditional
CNN
classication
methods.
Shetty
et
al.
[19]
proposed
a
methodology
for
the
classication
of
skin
lesion
photographs
emplo
ying
CNN
and
machine
learning
techniques,
with
outcomes
assessed
utilizing
the
HAM10000
dataset.
Dhi
vyaa
et
al.
[20]
inte
grated
learning
theory
wit
h
the
decision
tree-based
random
forest
cl
assication
methodology
to
enhance
the
accurac
y
and
rob
ustness
of
skin
lesion
image
cate
gorization.
Mahbod
et
al.
[21]
conducted
an
in
v
estig
ation
into
the
inuence
of
image
dimensions
on
the
ef
cac
y
of
transfer
learning
class
ication
in
the
conte
xt
of
skin
lesion
analysis.
Alenezi
et
al.
[22]
introduced
an
inno
v
ati
v
e
approach
for
the
classication
of
skin
lesions,
which
inte
grates
w
a
v
elet-based
preprocessing
techniques,
deep
residual
neural
netw
orks,
and
e
xtreme
learning
machine
classiers.
Ho
we
v
er
,
despite
these
adv
ancements
in
skin
cancer
detection
and
diagnosis,
indi
viduals
in
rural
areas
continue
to
f
ace
signicant
barriers
to
timely
and
accurate
healthcare
access.
Current
technologies,
including
telemedicine
and
e
xisting
mobile
health
solutions,
often
f
all
short
in
pro
viding
the
necessary
high-resolution
imaging
and
rob
ust
computational
resources
needed
for
precise
skin
cancer
classication.
Ben-Bouazza
et
al.
[23]
there
is
a
noticeable
lack
of
inte
gration
between
mobile
imaging
de
vices
and
cloud-based
deep
learning
models
that
can
bridge
the
diagnostic
g
ap
between
urban
and
rural
popu
l
ations.
This
g
ap
underscores
the
need
for
an
architecture
that
not
only
f
acilitates
high-quality
dermoscopic
imaging
b
ut
also
ensures
seamless
data
transfer
and
analysis
in
cloud
en
vironments.
Furthermore,
there
is
a
critical
need
for
a
system
that
deli
v
ers
di-
agnostic
insights
to
remote
healthcare
pro
viders,
ensuring
that
patients
in
underserv
ed
areas
recei
v
e
comparable
le
v
els
of
care
to
those
in
urban
centers
[4].
In
light
of
these
shortcomings,
this
paper
proposes
a
ne
w
architec-
ture
aimed
at
impro
ving
skin
cancer
classication,
specically
focusing
on
indi
viduals
in
rural
areas
who
may
ha
v
e
limited
access
to
healthcare.
The
architecture
combines
a
high-resolution
mobile
camera
designed
for
dermoscop
y
with
a
deep
learning
model
hosted
on
the
cloud.
Ben-Bouazza
et
al.
[1]
this
setup
enables
not
only
the
capture
and
analysis
of
dermoscopic
images
b
ut
also
the
seamless
transfer
of
data
to
cloud
serv
ers
equipped
with
ample
computational
res
ources
for
thorough
analysis.
The
resulting
insights
are
then
shared
with
medical
centers,
allo
wing
healthcare
professionals
to
remotely
access
diagnostic
results.
This
method
ensures
that
pa-
tients
in
rural
areas
recei
v
e
the
same
le
v
el
of
diagnostic
scrutin
y
as
those
in
urban
settings,
thereby
closing
a
signicant
g
ap
in
healthcare
accessibility
[12].
This
research
has
the
potential
to
signicantly
impro
v
e
early
diagnosis
and
timely
interv
ention
for
skin
cancer
,
particularly
in
underserv
ed
rural
communities
where
access
to
specialized
healthcare
is
limited.
The
proposed
deep
learning
m
od
e
l
is
instrumental
in
f
acilitating
a
thorough
e
xaminati
on
of
skin
lesions
with
enhanced
precision
and
ef
cienc
y
,
f
ar
surpassing
traditional
approaches
that
often
rely
on
manual
analysis
[24].
By
enabling
timely
detection
and
ef
cie
nt
diagnostic
processes,
the
system
ensures
that
treatment
can
commence
promptly
,
leading
to
better
patient
outcomes
and
potentially
reducing
mortality
rates
associated
with
skin
cancer
.
Be
yond
its
academic
contrib
utions,
this
research
of
fers
practical
applications
in
real-w
orld
scenarios,
such
as
mobile
clinics
and
telehealth
platforms,
leading
to
positi
v
e
impacts
on
global
health
outcomes
and
equity
in
healthcare
access
[25].
The
remainder
of
this
paper
is
or
g
anized
as
follo
ws.
I
n
section
2,
the
methods
and
Materials
section
pro
vides
a
detailed
account
of
the
techniques
and
technologies
used
in
the
study
,
including
the
unique
architecture
proposed
for
classifying
skin
cancer
.
This
section
also
co
v
ers
the
w
orko
w
of
Digiscope
in
Node-Red,
demonstrating
ho
w
data
is
processed
and
analyzed
in
a
real-time
en
vironment.
The
Data
section
within
this
part
gi
v
es
a
comprehensi
v
e
o
v
ervie
w
of
the
types
and
sources
of
data
utilized,
with
a
specic
emphasis
on
dermoscopic
images
obtained
from
rural
areas.
Section
3
presents
the
results
and
discussion,
e
v
aluating
the
ef
fecti
v
eness
of
t
he
proposed
methods.
Section
4
discusses
the
challenges
and
limitations
encountered
during
the
study
.
Finally
,
section
5
pro
vides
the
conclusion,
encapsulates
the
principal
disco
v
eries
and
proposes
possible
directions
for
subsequent
in
v
estig
ations.
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
40,
No.
1,
October
2025:
202–215
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
205
2.
MA
TERIALS
AND
METHODS
2.1.
Dataset
:
HAM10000
2.1.1.
Data
settings
The
HAM10000
dataset
comprises
a
total
of
10015
dermatoscopic
images,
which
were
meticulous
ly
g
athered
o
v
er
a
span
of
tw
o
decades
from
distinct
locations
.
Specically
,
these
images
were
procured
from
tw
o
prominent
sites:
the
esteemed
Department
of
Dermatology
at
the
Medical
Uni
v
ers
ity
of
V
ienna,
Austria,
and
the
reputable
skin
cancer
practice
of
Clif
f
Rosendahl
situated
in
Queensland,
Australia.
Gajera
et
al.
[4]
the
Australian
platform
ef
fecti
v
ely
emplo
yed
Po
werPoint
les
and
Excel
databases
for
the
purpose
of
storing
both
images
and
meta-data.
The
Austrian
site
commenced
the
process
of
amassing
visual
representations
prior
to
the
adv
ent
of
digit
al
cameras,
and
subsequently
preserv
ed
said
images
alongside
corresponding
metadata
in
di
v
erse
formats
across
v
arying
temporal
epochs.
The
lesion
is
positioned
at
the
centre
of
the
im
age,
precisely
at
coordinates
800x600
pix
els,
with
a
resolution
of
72
dots
per
inch
(DPI).
The
entirety
of
the
data
records
pertaining
to
the
HAM
1
0000
dataset
has
been
archi
v
ed
within
the
Harv
ard
Data
v
erse
repository
.
T
able
1
presents
a
comprehensi
v
e
summary
of
the
image
count
within
the
HAM10000
training
set,
cate
gorized
by
diagnosis,
and
juxtaposed
with
data
from
e
xisting
databases.
The
images
and
associated
metadata
can
be
accessed
via
the
public
ISIC
archi
v
e,
both
through
the
archi
v
e
g
allery
and
through
standardized
API
calls
(https://isic-archi
v
e.com/api/v1).
T
able
1.
Summary
of
dermatological
datasets:
total
images,
pathologic
v
erication
percentages,
and
class
distrib
ution
Dataset
T
otal
images
P
athologic
v
erication
akiec
bcc
bkl
df
mel
n
v
v
ast
PH2
200
20.5%
-
-
-
-
40
160
-
Atlas
1024
unkno
wn
5
42
70
20
275
582
30
ISIC
2017
13786
26.3%
2
33
575
7
1019
11861
15
Rosenthal
2259
100%
295
296
490
30
342
803
3
V
iDIR
Le
g
ac
y
439
100%
0
5
10
4
67
350
3
V
iDIR
MoleMax
3954
1.2%
0
2
124
30
24
3720
54
HAM10000
10015
53.3%
327
514
1099
115
1113
6705
142
The
HAM10000
dataset,
comprising
10,015
images
of
v
arious
skin
lesions
cate
gorized
into
se
v
en
dif
ferent
classes
[26].
The
classes
are
visually
depicted
in
Figure
1.
Figure
1.
HAM10000
database
classes
2.1.2.
Data
pr
eparation
Applying
a
range
of
transformations
to
e
xisting
images
is
a
common
practice
in
skin
cancer
imaging
to
e
xpand
and
di
v
ersify
the
dataset.
V
arious
techniques
are
emplo
yed
to
create
dif
ferent
v
ariations
of
images,
in-
cluding
rotation,
ipping,
scaling,
cropping,
and
color
adjustment
lik
e
sho
wn
in
Figure
2,
this
process
enhances
the
reliability
and
prec
ision
of
machine
learning
models
by
enabling
them
to
learn
from
a
wider
v
ariety
of
data,
DigiScope:
IoT
-enhanced
deep
learning
for
skin
cancer
pr
o
gnosis
(Edder
A
ymane)
Evaluation Warning : The document was created with Spire.PDF for Python.
206
❒
ISSN:
2502-4752
minimizing
o
v
ertting
and
impro
ving
their
capacity
to
generalize
to
unf
amiliar
images.
Data
augmentation
plays
a
vital
role
in
tackling
the
limited
a
v
ailability
of
label
ed
medical
images
and
enhancing
the
ef
fecti
v
eness
of
skin
cancer
detection
algorithms.
Figure
2.
Data
augmentation
e
xample
2.2.
W
orko
w
of
the
pr
oposed
appr
oach
This
o
wchart
in
Figure
3
illustrat
es
the
w
orko
w
for
a
deep
learning
project
focused
on
skin
cancer
classication
using
the
HAM10000
dataset.
−
HAM-10000:
HAM10000
is
a
dataset
conta
ining
images
of
skin
lesions,
used
for
training
and
testing
the
model.
−
Pre-processing:
the
ra
w
data
from
HAM10000
is
pre-processed.
Pre-processing
might
include
tasks
such
as
normalization,
resizing
images,
data
augmentation,
and
other
techniques
to
prepare
the
data
for
training
the
model.
−
T
raining
model:
after
pre-processing,
the
data
is
fed
into
a
machine
learning
model
for
training.
This
in
v
olv
es
using
algorithms
to
learn
patterns
from
the
training
data.
−
Classication:
the
trained
model
is
then
used
for
classication.
This
is
where
the
model
mak
es
predictions
on
ne
w
,
unseen
data.
−
Y
es
(Successful
classication):
if
the
classication
results
are
satisf
actory
,
the
w
orko
w
proceeds
to
deplo
yment.
−
No
(Unsuccessful
classication):
if
the
classication
results
are
not
satisf
actory
,
the
w
orko
w
mo
v
es
to
the
results
analysis
phase.
−
Results
analysis:
here,
the
results
of
the
classication
are
analyzed.
This
step
in
v
olv
es
assessing
the
perfor
-
mance
of
the
model,
identifying
an
y
shortcomings,
and
understanding
the
reasons
behind
incorrect
classi-
cations.
−
Hyperparameters
update:
based
on
the
analysis,
the
model’
s
h
yperparameters
are
updated.
Hyperparameter
tuning
is
crucial
for
impro
ving
model
performance.
Once
updated,
the
model
is
retrained
with
the
ne
w
settings.
−
Deplo
yment:
if
the
classication
is
successful,
the
model
is
deplo
yed.
Deplo
yment
means
inte
grating
the
model
into
a
production
en
vironment
where
it
can
be
used
for
real-time
predictions.
−
Optimization:
after
deplo
yment,
the
model
is
further
optimized
to
enhance
its
performance
and
ef
cienc
y
in
the
production
en
vironment.
−
Real-w
orld
inte
gration:
the
nal
step
in
v
olv
es
inte
grating
the
optimi
zed
model
into
real-w
orld
applications,
making
it
accessible
for
end-users
and
ensuring
it
performs
well
in
practical
scenarios.predictions.
This
w
orko
w
is
iterati
v
e,
with
the
loop
between
results
analysis,
h
yperparameters
update,
and
model
training
ensuring
continuous
impro
v
ement
until
satisf
actory
classication
performance
is
achie
v
ed.
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
40,
No.
1,
October
2025:
202–215
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
207
Figure
3.
W
orko
w
of
the
proposed
approach
2.3.
W
orko
w
of
the
pr
oposed
ar
chitectur
e
In
this
scientic
paper
,
we
de
vised
an
entirely
autonomous
methodology
that
harnesses
the
po
wer
of
CNNs
to
discern
and
classify
cutaneous
anomalies
with
utmost
precision.
The
central
emphasis
of
our
study
re
v
olv
ed
around
the
e
xploration
and
e
v
aluation
of
ef
cacious
pre-processing
methodologies
and
classication
algorithms.
In
order
to
assess
the
ef
cac
y
of
our
methodology
,
we
utilised
the
HAM10000
dataset,
which
encompasses
a
total
of
10,015
di
v
erse
images
depicting
a
wide
range
of
ski
n
lesions
that
ha
v
e
been
meticulously
classied
into
se
v
en
distinct
cate
gories.
The
sequential
procedure
that
we
emplo
yed
is
graphically
represented
in
Figure
4.
In
the
subsequent
section,
we
shall
embark
upon
an
in-depth
e
xploration
of
the
data
emplo
yed
in
this
study
,
elucidating
the
preprocessing
procedures
that
were
implemented.
Furthermore,
we
shall
delv
e
into
the
proposed
theoretical
frame
w
ork,
meticulously
e
xamining
its
intricate
components
and
scrutinising
its
h
yperparameters.
Figure
4.
W
orko
w
of
the
proposed
architecture
2.3.1.
The
pr
oposed
ar
chitectur
e
In
contrast
to
a
traditional
neural
netw
ork,
a
CNN
is
designed
to
elucidate
intricate
patterns
through
the
direct
application
of
lters
to
the
unprocessed
pix
els
of
an
image.
W
e
used
the
Python
libraries
T
ensoro
w
and
K
eras
for
our
project
to
de
v
elop
and
implement
the
CNN
model.
T
able
2
pro
vides
an
o
v
ervie
w
of
the
layers
and
h
yperparameters
utilized
in
our
netw
ork.
These
layers
and
h
yperparameters
play
a
crucial
role
in
dening
the
structure
and
beha
vior
of
the
CNN
model.
DigiScope:
IoT
-enhanced
deep
learning
for
skin
cancer
pr
o
gnosis
(Edder
A
ymane)
Evaluation Warning : The document was created with Spire.PDF for Python.
208
❒
ISSN:
2502-4752
T
able
2.
CNN
layers
and
h
yperparameters
Layer
Hyperparameters
Con
v2D
16
ltres,
3x3
lter
size,
ReLu
acti
v
ation,
same
padding
Con
v2D
32
ltres,
3x3
lter
size,
ReLu
acti
v
ation,
MaxPool2D
2x2
pool
size
Con
v2D
32
ltres,
3x3
lter
size,
ReLu
acti
v
ation,
same
padding
Con
v2D
64
ltres,
3x3
lter
size,
ReLu
acti
v
ation,
same
padding
MaxPool2D
2x2
pool
size,
same
padding
Flatten
2304
units
Dense
64
units,
ReLu
acti
v
ation
Dense
32
units,
ReLu
acti
v
ation
Dense
7
units,
SoftMax
acti
v
ation
2.3.2.
Model
h
yper
parameters
W
e
carefully
selected
specic
commonly
used
h
yperparameter
v
alues
to
ensure
a
more
accurate
e
v
al-
uation
of
our
model.
T
able
3
highlights
the
specic
h
yp
e
rparameter
v
alues
emplo
yed
in
our
CNN
model.
The
follo
wing
section
e
xplains
the
rationale
behind
selecting
these
v
alues
in
our
approach.
By
choosing
appropriate
h
yperparameter
v
alues,
we
aimed
to
optimize
the
performance
and
ef
fecti
v
eness
of
our
CNN
model
for
skin
lesion
identication
and
classication:
−
Optimizer:
Adam
w
as
selected
as
the
optimization
technique
for
training
deep
neural
netw
orks
due
to
its
f
acile
to
use
nature,
computational
ef
cienc
y
,
and
ef
cac
y
in
managing
substantial
v
olumes
of
data
and
parameters.
−
Loss
function:
the
loss
function
emplo
yed
in
the
multi-clas
s
scenario
is
deri
v
ed
from
the
“sparse
cate
gorical
cross-entrop
y”
methodology
,
which
f
acilitates
the
computation
of
the
loss
v
alue.
−
Epochs:
the
epoch
count
is
set
at
50.
This
w
as
determined
through
e
xperimentation,
which
found
that
50
epochs
resulted
in
a
model
with
lo
w
loss
and
no
o
v
ertting
to
the
training
set
(or
the
least
amount
of
o
v
ertting
possible).
−
Batch
size:
a
series
of
preliminary
e
xperiments
were
conducted
utilizing
batch
sizes
of
20,
30,
60,
and
90,
with
the
ndings
indicating
that
a
batch
size
of
128
yielded
the
most
f
a
v
orable
outcomes.
T
able
3.
CNN
model’
s
h
yperparameters
Hyperparametres
V
alue
Optimizer
Adam
Loss
function
Sparse
cate
gorical
cross-entrop
y
Epochs
50
Batch
size
128
2.4.
DigiScope
framew
ork
2.4.1.
The
pr
oposed
edge-AI
framew
ork
the
Digiscope
edge-AI
frame
w
ork
is
a
no
v
el
medical
AI
paradigm
that
uses
self-learning
and
lar
ge-
scale
data
e
v
olution.
So
in
this
Figure
5
we
illustrates
a
system
for
managing
skin
disease
data
using
IoT
and
cloud
technologies,
di
vided
into
three
main
parts:
−
Edge
de
vices:
the
edge
de
vices
section
includes
v
arious
de
vices
such
as
dermatoscopic
cameras,
smart-
phones,
smartw
atches,
and
other
IoT
de
vices.
These
de
vices
are
responsible
for
collecting
data
related
to
skin
diseases,
including
images
and
other
health
metrics.
Once
collected,
the
data
is
transmitted
to
the
cloud
using
secure
communication
protoc
o
l
s
f
acilitated
by
routers,
ensuring
that
the
data
is
sent
ef
ciently
and
securely
.
−
Cloud:
the
cloud
section
represents
the
cloud
infrastructure,
which
includes
storage,
processing
units,
and
machine
learning
models.
When
data
from
the
edge
de
vices
reaches
the
cloud,
it
is
stored
and
processed.
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
40,
No.
1,
October
2025:
202–215
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
209
The
cloud
infrastructure
uses
machine
learning
algorithms
to
analyze
the
data,
pro
viding
insights
and
up-
dates.
The
cloud
also
updates
the
model
parameters
based
on
ne
w
data,
ensuring
that
the
analysis
remains
accurate
and
up-to-date.
The
results
of
the
data
processing
are
then
sent
back
to
the
edge
de
vices
and
forw
arded
to
the
online
medical
services.
−
Online
medical
services:
the
online
medical
services
section
includes
v
arious
healthca
re
services
such
as
telemedicine
platforms,
hospitals,
amb
ulances,
and
healthcare
pro
viders.
These
services
utilize
the
pro-
cessed
data
and
insights
pro
vided
by
the
cloud
to
of
fer
medical
advice,
diagnosis,
and
treatment
options.
By
inte
grating
the
data
from
the
cloud,
healthcare
professionals
can
access
real-time
updates
and
therapeu-
tic
protocols,
which
helps
in
impro
ving
patient
care
and
outcomes.
This
part
of
the
system
ensures
that
the
processed
data
is
ef
fecti
v
ely
used
to
pro
vide
timely
and
accurate
medical
services
to
patients.
This
inte
grated
system
allo
ws
for
ef
cient
data
collection,
processing,
and
utilization,
thereby
enhancing
the
management
and
treatment
of
skin
diseases
through
a
connected
and
intelligent
infrastructure.
Figure
5.
Digiscope:
medical
edge-AI
frame
w
ork
2.4.2.
Digiscope
w
orko
w
in
node-RED
This
Figure
6
illustrates
the
theoretical
transfer
of
skin
cancer
picture
data
via
a
secure
communicati
o
n
technique
from
a
dermatoscopic
camera
to
the
cloud
for
p
r
ocessing.
It
illustrates
projected
data
tra
v
el.
The
images
are
transmitted
from
edge
de
vices
to
Google
Cloud
via
MQTT
,
ensuring
secure
and
ef
cient
data
transfer
.
The
MQTT
brok
er
publishes
the
data
to
a
pub/sub
system,
which
forw
ards
it
to
a
vision
module
for
processing.
The
processed
data
is
then
sent
to
an
AutoML
module
for
machine
learning
analysis.
The
analysis
results
update
the
IoT
conguration
and
send
commands
back
through
the
pub/sub
system
to
the
MQTT
brok
er
for
terminal
visualization.
This
simulation
helps
conceptualize
the
architecture,
with
real
data
o
w
planned
for
future
projects.
Figure
6.
Digiscope
w
orko
w
in
node-RED
3.
RESUL
TS
AND
DISCUSSIONS
In
the
present
study
,
we
implemented
a
CNN
algorithm
on
a
computational
platform
with
a
16-
gig
abyte
(GB)
random
access
memory
(RAM)
and
an
Intel
i7-8650U
processor
.
This
setup
f
acilitated
ef
cient
data
processing
and
e
x
ecution
of
the
CNN
algorithm,
with
training
a
v
eraging
25
minutes
and
classication
of
a
single
sample
taking
approximately
0.130
milliseconds.
Python
w
as
utilized
for
implementation,
emplo
ying
libraries
such
as
K
eras,
P
andas,
and
Scikit-Learn.
The
model
demonstrated
remarkable
ef
cac
y
,
achie
ving
DigiScope:
IoT
-enhanced
deep
learning
for
skin
cancer
pr
o
gnosis
(Edder
A
ymane)
Evaluation Warning : The document was created with Spire.PDF for Python.
210
❒
ISSN:
2502-4752
an
o
v
erall
precision
rate
of
98%
on
an
independent
test
dataset
and
a
loss
rate
of
19%
during
50
epochs
of
training,
with
minimal
signs
of
o
v
ertting.
Notably
,
data
augmentation
techniques
enhanced
model
accurac
y
.
The
ndings
underscore
the
ef
cac
y
of
deep
learning
models
in
the
precise
classication
of
skin
lesions
within
practical,
real-w
orld
conte
xts.
This
research
presents
a
comparati
v
e
e
xamination
of
our
deep
learning
model
in
relation
to
estab-
lished
methodologies
for
the
classication
of
skin
lesions.
re
v
ealing
superior
accurac
y
and
speed
compared
to
con
v
entional
techniques.
The
CNN
algorithm’
s
performance,
as
assessed
by
metrics
such
as
recall,
preci-
sion,
F1-score,
and
support,
wich
can
be
calculated
by
the
v
alues
sho
wn
in
Figure
7,
demonstrated
comparable
results
to
the
SVM
algorithm.
Ho
we
v
er
,
our
approach
e
xcels
in
ef
ciently
identifying
positi
v
e
instances
and
minimizing
f
alse
positi
v
es,
as
sho
wn
in
T
ables
4-6.
The
ndings
align
with
pre
vious
studies
that
emphasize
the
benets
of
deep
learning
for
skin
lesion
classication.
Despite
its
strengths,
our
study
has
limitations,
such
as
potential
biases
in
the
training
data
and
the
need
for
further
v
alidation
in
di
v
erse
clinical
settings.
Une
xpect-
edly
,
the
CNN
model
e
xhibited
a
notably
lo
w
loss
rate
with
data
augmentation,
underscoring
its
rob
ustness
in
v
arious
conditions.
Figure
7.
Multi-class
confusion
matrix
of
the
customised
CNN
model
T
able
4.
Multi-class
classication
report
of
the
customised
CNN
model
Precision
Recall
F1-score
Support
0-n
v
0,99
1,00
1,00
1359
1-mel
0,98
1,00
0,99
1318
2-bkl
0,96
0,98
0,97
1262
3-bcc
1,00
1,00
1,00
1351
4-v
asc
0,99
0,88
0,93
1374
5-akiec
1,00
1,00
1,00
1358
6-df
0,94
0,99
0,97
1365
macro
a
vg
0,98
0,98
0,98
9387
weighted
a
vg
0,98
0,98
0,98
9387
T
able
5.
Metrics
model
Metrics
Classication
Dense
SVM
Accurac
y
(%)
0,98
0,98
Precision
(%)
0,98
0,98
Recall
(%)
0,98
0,98
F1-score
(%)
0,98
0,98
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
40,
No.
1,
October
2025:
202–215
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
211
T
able
6.
CNN
model
learning
results
CNN
model
Accurac
y
(%)
Loss
(%)
T
est
set
0,98
0,19
The
principal
objecti
v
e
of
this
in
v
estig
ation
w
as
to
e
xamine
the
ef
cac
y
of
deep
learning
algorithms
in
the
classication
of
dermal
lesions,
with
the
results
suggesting
considerable
promise
for
practical
clinical
imple-
mentation.
This
study
highlights
the
critical
role
of
emplo
ying
sophisticated
computational
methodologies
for
the
early
identication
and
management
of
skin
cancer
,
potentially
resulting
in
enhanced
patient
outcomes
and
diminished
healthcare
e
xpenditures.
Nonetheless,
se
v
eral
inquiries
persist,
particularly
re
g
arding
the
model’
s
generalizability
across
di
v
erse
populations
and
the
incorporation
of
t
hese
systems
into
clinical
practice.
Sub-
sequent
in
v
estig
ations
ought
to
concentrate
on
mitig
ating
these
deciencies
and
enhancing
models
for
more
e
xtensi
v
e
applicability
.
Through
the
inte
gration
of
the
ndings
presented
in
this
study
,
we
can
f
acilitate
the
progression
of
inno
v
ati
v
e
diagnostic
instruments
within
the
eld
of
dermatology
.
The
e
v
aluation
of
our
proposed
model
ag
ainst
contemporary
methodologies
utilizing
the
HAM10000
dataset
re
v
eals
its
enhanced
performance
with
respect
to
accurac
y
.
As
illustrated
in
T
able
7
and
Figures
8
and
9,
which
depict
the
accurac
y
and
loss
curv
es
respecti
v
ely
,
the
proposed
model
attained
an
accurac
y
of
98%,
thereby
signicantly
surpassing
multiple
well-established
architectures.
F
or
e
xample,
InceptionV3
and
Xcep-
tion,
recognized
for
their
strong
feature
e
xtraction
abilities,
achie
v
ed
accuracies
of
91.56%
and
91.47%,
respec-
ti
v
ely
.
In
a
comparable
analysis,
InceptionResNetV2,
recognized
as
a
leading
model
in
the
eld,
achie
v
ed
an
accurac
y
of
93.20%,
whi
ch
remains
signicantly
inferior
to
the
performance
metr
ics
of
the
model
we
propose.
Alternati
v
e
methodologies,
such
as
Shifted
2-Nets
and
EW
-FCM+wide-shuf
enet,
demonstrated
e
v
en
lo
wer
accurac
y
rates,
recording
83.20%
and
84.80%,
respecti
v
ely
.
The
ndings
underscore
t
h
e
ef
fecti
v
eness
of
the
proposed
methodology
,
demonstrating
a
signicant
enhancement
compared
to
con
v
entional
techniques
in
the
classication
of
dermatological
images.
The
notable
impro
v
ement
in
precision
can
be
asc
ribed
to
the
model’
s
capacity
to
discern
comple
x
patterns
and
character
istics
present
in
skin
lesion
images,
thereby
pro
viding
a
viable
approach
for
the
accurate
and
dependable
diagnosis
of
skin
lesions.
T
able
7.
The
proposed
w
ork
with
recent
e
xisting
techniques
on
the
HAM10000
dataset
Comparing
proposed
and
e
xisting
w
ork
Accurac
y
(%)
InceptionV3
[6]
91.56
InceptionResNetV2
[6]
93.20
Xception
[6]
91.47
Shifted
2-Nets
[27]
83.20
EW
-FCM+wide-shuf
enet
[3]
84.80
Proposed
model
98.00
Figure
8.
Accurac
y
of
the
customized
CNN
model
DigiScope:
IoT
-enhanced
deep
learning
for
skin
cancer
pr
o
gnosis
(Edder
A
ymane)
Evaluation Warning : The document was created with Spire.PDF for Python.