Middle East Journal of Science,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 18, 2024
This
study
investigates
the
effectiveness
of
MobileNetV2
transfer
learning
method
and
a
deep
based
Convolutional
Neural
Network
(CNN)
model
in
categorization
malignant
benign
skin
lesions
cancer
diagnosis.
Since
is
disease
that
can
be
cured
with
early
detection
but
fatal
if
delayed,
accurate
diagnosis
great
importance.
The
was
trained
architecture
performed
classification
task
high
accuracy
on
images
lesions.
Metrics
such
as
accuracy,
recall,
precision
F1
score
obtained
during
training
validation
processes
support
performance
model.
92.97%,
Recall
92.71%,
Precision
94.70%
93.47%.
results
show
CNN-based
reliable
effective
tool
for
diagnosis,
small
fluctuations
phase
require
further
data
hyperparameter
optimization
to
improve
generalization
ability
demonstrates
models
enhanced
offer
powerful
solution
medical
image
problems
have
potential
contribute
development
systems
healthcare
field.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Фев. 10, 2025
Skin
cancer
represents
a
significant
global
health
concern,
where
early
and
precise
diagnosis
plays
pivotal
role
in
improving
treatment
efficacy
patient
survival
rates.
Nonetheless,
the
inherent
visual
similarities
between
benign
malignant
lesions
pose
substantial
challenges
to
accurate
classification.
To
overcome
these
obstacles,
this
study
proposes
an
innovative
hybrid
deep
learning
model
that
combines
ConvNeXtV2
blocks
separable
self-attention
mechanisms,
tailored
enhance
feature
extraction
optimize
classification
performance.
The
inclusion
of
initial
two
stages
is
driven
by
their
ability
effectively
capture
fine-grained
local
features
subtle
patterns,
which
are
critical
for
distinguishing
visually
similar
lesion
types.
Meanwhile,
adoption
later
allows
selectively
prioritize
diagnostically
relevant
regions
while
minimizing
computational
complexity,
addressing
inefficiencies
often
associated
with
traditional
mechanisms.
was
comprehensively
trained
validated
on
ISIC
2019
dataset,
includes
eight
distinct
skin
categories.
Advanced
methodologies
such
as
data
augmentation
transfer
were
employed
further
robustness
reliability.
proposed
architecture
achieved
exceptional
performance
metrics,
93.48%
accuracy,
93.24%
precision,
90.70%
recall,
91.82%
F1-score,
outperforming
over
ten
Convolutional
Neural
Network
(CNN)
based
Vision
Transformer
(ViT)
models
tested
under
comparable
conditions.
Despite
its
robust
performance,
maintains
compact
design
only
21.92
million
parameters,
making
it
highly
efficient
suitable
deployment.
Proposed
Model
demonstrates
accuracy
generalizability
across
diverse
classes,
establishing
reliable
framework
clinical
practice.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Ноя. 27, 2024
Cervical
cancer
is
the
second
most
common
in
women's
bodies
after
breast
cancer.
develops
from
dysplasia
or
cervical
intraepithelial
neoplasm
(CIN),
early
stage
of
disease,
and
characterized
by
aberrant
growth
cells
cervix
lining.
It
primarily
caused
Human
Papillomavirus
(HPV)
infection,
which
spreads
through
sexual
activity.
This
study
focuses
on
detecting
types
efficiently
using
a
novel
lightweight
deep
learning
model
named
CCanNet,
combines
squeeze
block,
residual
blocks,
skip
layer
connections.
SipakMed,
not
only
popular
but
also
publicly
available
dataset,
was
used
this
study.
We
conducted
comparative
analysis
between
several
transfer
transformer
models
such
as
VGG19,
VGG16,
MobileNetV2,
AlexNet,
ConvNeXT,
DeiT_tiny,
MobileViT,
Swin
Transformer
with
proposed
CCanNet.
Our
outperformed
other
state-of-the-art
models,
98.53%
accuracy
lowest
number
parameters,
1,274,663.
In
addition,
accuracy,
precision,
recall,
F1
score
were
to
evaluate
performance
models.
Finally,
explainable
AI
(XAI)
applied
analyze
CCanNet
ensure
results
trustworthy.
