Breakthroughs
in
skin
cancer
diagnostics
have
resulted
from
recent
image
recognition
and
Artificial
Intelligence
(AI)
technology
advancements.
There
has
been
growing
that
can
be
lethal
to
humans.
For
instance,
melanoma
is
the
most
unpredictable
terrible
form
of
cancer.
Frontiers in Medicine,
Год журнала:
2023,
Номер
10
Опубликована: Апрель 5, 2023
Renal
diseases
are
common
health
problems
that
affect
millions
of
people
around
the
world.
Among
these
diseases,
kidney
stones,
which
anywhere
from
1
to
15%
global
population
and
thus;
considered
one
leading
causes
chronic
(CKD).
In
addition
renal
cancer
is
tenth
most
prevalent
type
cancer,
accounting
for
2.5%
all
cancers.
Artificial
intelligence
(AI)
in
medical
systems
can
assist
radiologists
other
healthcare
professionals
diagnosing
different
(RD)
with
high
reliability.
This
study
proposes
an
AI-based
transfer
learning
framework
detect
RD
at
early
stage.
The
presented
on
CT
scans
images
microscopic
histopathological
examinations
will
help
automatically
accurately
classify
patients
using
convolutional
neural
network
(CNN),
pre-trained
models,
optimization
algorithm
images.
used
CNN
models
VGG16,
VGG19,
Xception,
DenseNet201,
MobileNet,
MobileNetV2,
MobileNetV3Large,
NASNetMobile.
addition,
Sparrow
search
(SpaSA)
enhance
model's
performance
best
configuration.
Two
datasets
were
used,
first
dataset
four
classes:
cyst,
normal,
stone,
tumor.
case
latter,
there
five
categories
within
second
relate
severity
tumor:
Grade
0,
1,
2,
3,
4.
DenseNet201
MobileNet
four-classes
compared
others.
Besides,
SGD
Nesterov
parameters
optimizer
recommended
by
three
while
two
only
recommend
AdaGrad
AdaMax.
five-class
dataset,
Xception
best.
Experimental
results
prove
superiority
proposed
over
state-of-the-art
classification
models.
records
accuracy
99.98%
(four
classes)
100%
(five
classes).
Diagnostics,
Год журнала:
2023,
Номер
13(10), С. 1815 - 1815
Опубликована: Май 22, 2023
When
it
comes
to
skin
tumors
and
cancers,
melanoma
ranks
among
the
most
prevalent
deadly.
With
advancement
of
deep
learning
computer
vision,
is
now
possible
quickly
accurately
determine
whether
or
not
a
patient
has
malignancy.
This
significant
since
prompt
identification
greatly
decreases
likelihood
fatal
outcome.
Artificial
intelligence
potential
improve
healthcare
in
many
ways,
including
diagnosis.
In
nutshell,
this
research
employed
an
Inception-V3
InceptionResnet-V2
strategy
for
recognition.
The
feature
extraction
layers
that
were
previously
frozen
fine-tuned
after
newly
added
top
trained.
study
used
data
from
HAM10000
dataset,
which
included
unrepresentative
sample
seven
different
forms
cancer.
To
fix
discrepancy,
we
utilized
augmentation.
proposed
models
outperformed
results
previous
investigation
with
effectiveness
0.89
0.91
InceptionResnet-V2.
Diagnostics,
Год журнала:
2023,
Номер
13(3), С. 486 - 486
Опубликована: Янв. 29, 2023
Wilms'
tumor,
the
most
prevalent
renal
tumor
in
children,
is
known
for
its
aggressive
prognosis
and
recurrence.
Treatment
of
multimodal,
including
surgery,
chemotherapy,
occasionally,
radiation
therapy.
Preoperative
chemotherapy
used
routinely
European
studies
select
indications
North
American
trials.
The
objective
this
study
was
to
build
a
novel
computer-aided
prediction
system
preoperative
response
tumors.
A
total
63
patients
(age
range:
6
months-14
years)
were
included
study,
after
receiving
their
guardians'
informed
consent.
We
incorporated
contrast-enhanced
computed
tomography
imaging
extract
texture,
shape,
functionality-based
features
from
tumors
before
chemotherapy.
proposed
consists
six
steps:
(i)
delineate
tumors'
images
across
three
contrast
phases;
(ii)
characterize
texture
using
first-
second-order
textural
features;
(iii)
shape
by
applying
parametric
spherical
harmonics
model,
sphericity,
elongation;
(iv)
capture
intensity
changes
phases
describe
functionality;
(v)
apply
fusion
based
on
extracted
(vi)
determine
final
as
responsive
or
non-responsive
via
tuned
support
vector
machine
classifier.
achieved
an
overall
accuracy
95.24%,
with
95.65%
sensitivity
94.12%
specificity.
Using
along
integrated
led
superior
results
compared
other
classification
models.
This
integrates
markers
learning
model
make
early
predictions
about
how
will
respond
can
lead
personalized
management
plans
International Journal of Systems Science,
Год журнала:
2023,
Номер
55(4), С. 814 - 832
Опубликована: Дек. 28, 2023
The
sparrow
search
algorithm
(SSA)
is
an
efficient
swarm-intelligence-based
that
has
made
some
significant
advances
since
its
introduction
in
2020.
A
detailed
overview
of
the
basic
SSA
and
several
SSA-based
variants
presented
this
paper.
To
be
specific,
first,
principle
introduced
including
mechanism
implementation
process.
Second,
many
improved
SSAs
are
reviewed
hybrid,
chaotic,
adaptive,
binary
multi-objective
SSAs.
In
addition,
applications
real
scenarios
such
as
machine
learning
areas,
energy
systems,
path
planning
image
processing.
Finally,
further
research
directions
discussed.
This
survey
paper
aims
to
provide
a
timely
review
on
latest
developments
Diagnostics,
Год журнала:
2024,
Номер
14(4), С. 454 - 454
Опубликована: Фев. 19, 2024
In
recent
years,
there
has
been
growing
interest
in
the
use
of
computer-assisted
technology
for
early
detection
skin
cancer
through
analysis
dermatoscopic
images.
However,
accuracy
illustrated
behind
state-of-the-art
approaches
depends
on
several
factors,
such
as
quality
images
and
interpretation
results
by
medical
experts.
This
systematic
review
aims
to
critically
assess
efficacy
challenges
this
research
field
order
explain
usability
limitations
highlight
potential
future
lines
work
scientific
clinical
community.
study,
was
carried
out
over
45
contemporary
studies
extracted
from
databases
Web
Science
Scopus.
Several
computer
vision
techniques
related
image
video
processing
diagnosis
were
identified.
context,
focus
process
included
algorithms
employed,
result
accuracy,
validation
metrics.
Thus,
yielded
significant
advancements
using
deep
learning
machine
algorithms.
Lastly,
establishes
a
foundation
research,
highlighting
contributions
opportunities
improve
effectiveness
learning.
Heliyon,
Год журнала:
2024,
Номер
10(5), С. e26415 - e26415
Опубликована: Фев. 18, 2024
Skin
cancer
is
a
prevalent
form
of
that
necessitates
prompt
and
precise
detection.
However,
current
diagnostic
methods
for
skin
are
either
invasive,
time-consuming,
or
unreliable.
Consequently,
there
demand
an
innovative
efficient
approach
to
diagnose
utilizes
non-invasive
automated
techniques.
In
this
study,
unique
method
has
been
proposed
diagnosing
by
employing
Xception
neural
network
optimized
using
Boosted
Dipper
Throated
Optimization
(BDTO)
algorithm.
The
deep
learning
model
capable
extracting
high-level
features
from
dermoscopy
images,
while
the
BDTO
algorithm
bio-inspired
optimization
technique
can
determine
optimal
parameters
weights
network.
To
enhance
quality
diversity
ISIC
dataset
utilized,
widely
accepted
benchmark
system
diagnosis,
various
image
preprocessing
data
augmentation
techniques
were
implemented.
By
comparing
with
several
contemporary
approaches,
it
demonstrated
outperforms
others
in
detecting
cancer.
achieves
average
precision
94.936%,
accuracy
94.206%,
recall
97.092%
surpassing
performance
alternative
methods.
Additionally,
5-fold
ROC
curve
error
have
presented
validation
showcase
superiority
robustness
method.
PLoS ONE,
Год журнала:
2024,
Номер
19(5), С. e0301275 - e0301275
Опубликована: Май 31, 2024
Skin
cancer
has
a
significant
impact
on
the
lives
of
many
individuals
annually
and
is
recognized
as
most
prevalent
type
cancer.
In
United
States,
an
estimated
annual
incidence
approximately
3.5
million
people
receiving
diagnosis
skin
underscores
its
widespread
prevalence.
Furthermore,
prognosis
for
afflicted
with
advancing
stages
experiences
substantial
decline
in
survival
rates.
This
paper
dedicated
to
aiding
healthcare
experts
distinguishing
between
benign
malignant
cases
by
employing
range
machine
learning
deep
techniques
different
feature
extractors
selectors
enhance
evaluation
metrics.
this
paper,
transfer
models
are
employed
extractors,
metrics,
selection
layer
designed,
which
includes
diverse
such
Univariate,
Mutual
Information,
ANOVA,
PCA,
XGB,
Lasso,
Random
Forest,
Variance.
Among
models,
DenseNet-201
was
selected
primary
extractor
identify
features
from
data.
Subsequently,
Lasso
method
applied
selection,
utilizing
approaches
MLP,
RF,
NB.
To
optimize
accuracy
precision,
ensemble
methods
were
best-performing
models.
The
study
provides
sensitivity
rates
87.72%
92.15%,
respectively.
Symmetry,
Год журнала:
2023,
Номер
15(7), С. 1369 - 1369
Опубликована: Июль 5, 2023
Skin
cancer
represents
one
of
the
most
lethal
and
prevalent
types
observed
in
human
population.
When
diagnosed
its
early
stages,
melanoma,
a
form
skin
cancer,
can
be
effectively
treated
cured.
Machine
learning
algorithms
play
crucial
role
facilitating
timely
detection
aiding
accurate
diagnosis
appropriate
treatment
patients.
However,
implementation
traditional
machine
approaches
for
disease
is
impeded
by
privacy
regulations,
which
necessitate
centralized
processing
patient
data
cloud
environments.
To
overcome
challenges
associated
with
privacy,
federated
emerges
as
promising
solution,
enabling
development
privacy-aware
healthcare
systems
diagnosis.
This
paper
presents
comprehensive
review
that
examines
obstacles
faced
conventional
explores
integration
context
privacy-conscious
prediction
systems.
It
provides
discussion
on
various
datasets
available
performance
comparison
techniques
lesion
prediction.
The
objective
to
highlight
advantages
offered
potential
addressing
concerns
realm