Technologies,
Год журнала:
2024,
Номер
12(10), С. 190 - 190
Опубликована: Окт. 3, 2024
The
precise
and
prompt
identification
of
skin
cancer
is
essential
for
efficient
treatment.
Variations
in
colour
within
lesions
are
critical
signs
malignancy;
however,
discrepancies
imaging
conditions
may
inhibit
the
efficacy
deep
learning
models.
Numerous
previous
investigations
have
neglected
this
problem,
frequently
depending
on
features
from
a
singular
layer
an
individual
model.
This
study
presents
new
hybrid
model
that
integrates
discrete
cosine
transform
(DCT)
with
multi-convolutional
neural
network
(CNN)
structures
to
improve
classification
cancer.
Initially,
DCT
applied
dermoscopic
images
enhance
correct
distortions
these
images.
After
that,
several
CNNs
trained
separately
Next,
obtained
two
layers
each
CNN.
proposed
consists
triple
feature
fusion.
initial
phase
involves
employing
wavelet
(DWT)
merge
multidimensional
attributes
first
CNN,
which
lowers
their
dimension
provides
time–frequency
representation.
In
addition,
second
concatenated.
Afterward,
subsequent
fusion
stage,
merged
first-layer
combined
second-layer
create
effective
vector.
Finally,
third
bi-layer
various
integrated.
Through
process
training
multiple
both
original
photos
DCT-enhanced
images,
retrieving
separate
layers,
incorporating
CNNs,
comprehensive
representation
generated.
Experimental
results
showed
96.40%
accuracy
after
trio-deep
shows
merging
can
diagnostic
accuracy.
outperforms
CNN
models
most
recent
studies,
thus
proving
its
superiority.
PLoS ONE,
Год журнала:
2025,
Номер
20(5), С. e0321803 - e0321803
Опубликована: Май 20, 2025
Skin
lesions,
including
various
abnormalities
and
potentially
fatal
skin
cancers,
require
early
detection
for
effective
treatment.
However,
current
methods
often
struggle
to
identify
the
precise
areas
responsible
these
after
model
dominance
dispersion.
To
address
this,
we
propose
a
novel
Transfer
Learning-based
framework
that
integrates
Optimized
RegNet
Synergy
architectures
Attention-Triplet
mechanisms—comprising
channel
attention,
squeeze-excitation
soft
attention—combined
with
an
advanced
Ensemble
Learning
strategy.
A
significant
gap
in
research
is
lack
of
techniques
optimal
weight
allocation
predictions.
Our
study
fills
this
by
introducing
Chi2
Weighted
(CWE)
method,
which
further
enhanced
into
Multi-Layer
id="M2">
Medical Sciences,
Год журнала:
2025,
Номер
13(2), С. 70 - 70
Опубликована: Июнь 1, 2025
Artificial
intelligence
(AI)
is
rapidly
transforming
diagnostic
approaches
in
different
fields
of
medical
sciences,
demonstrating
an
emerging
potential
to
revolutionize
dermatopathology
due
its
capacity
process
large
amounts
data
the
shortest
possible
time,
both
for
diagnosis
and
research
purposes.
Different
AI
models
have
been
applied
neoplastic
skin
diseases,
especially
melanoma.
However,
date,
very
few
studies
investigated
role
dermatoses.
Herein,
we
provide
overview
key
aspects
functioning,
focusing
on
applications.
Then,
summarize
all
existing
English-language
literature
about
applications
field
non-neoplastic
diseases:
superficial
perivascular
dermatitis,
psoriasis,
fungal
infections,
onychomycosis,
immunohistochemical
characterization
inflammatory
dermatoses,
differential
between
latter
mycosis
fungoides
(MF).
Finally,
discuss
main
challenges
related
implementation
pathology.
Technologies,
Год журнала:
2024,
Номер
12(10), С. 190 - 190
Опубликована: Окт. 3, 2024
The
precise
and
prompt
identification
of
skin
cancer
is
essential
for
efficient
treatment.
Variations
in
colour
within
lesions
are
critical
signs
malignancy;
however,
discrepancies
imaging
conditions
may
inhibit
the
efficacy
deep
learning
models.
Numerous
previous
investigations
have
neglected
this
problem,
frequently
depending
on
features
from
a
singular
layer
an
individual
model.
This
study
presents
new
hybrid
model
that
integrates
discrete
cosine
transform
(DCT)
with
multi-convolutional
neural
network
(CNN)
structures
to
improve
classification
cancer.
Initially,
DCT
applied
dermoscopic
images
enhance
correct
distortions
these
images.
After
that,
several
CNNs
trained
separately
Next,
obtained
two
layers
each
CNN.
proposed
consists
triple
feature
fusion.
initial
phase
involves
employing
wavelet
(DWT)
merge
multidimensional
attributes
first
CNN,
which
lowers
their
dimension
provides
time–frequency
representation.
In
addition,
second
concatenated.
Afterward,
subsequent
fusion
stage,
merged
first-layer
combined
second-layer
create
effective
vector.
Finally,
third
bi-layer
various
integrated.
Through
process
training
multiple
both
original
photos
DCT-enhanced
images,
retrieving
separate
layers,
incorporating
CNNs,
comprehensive
representation
generated.
Experimental
results
showed
96.40%
accuracy
after
trio-deep
shows
merging
can
diagnostic
accuracy.
outperforms
CNN
models
most
recent
studies,
thus
proving
its
superiority.
