Research Square (Research Square),
Год журнала:
2022,
Номер
unknown
Опубликована: Авг. 9, 2022
Abstract
Skin
cancer
is
the
most
common
form
of
cancer.
Hence,
lives
millions
people
are
affected
by
this
every
year.
Approximately,
it
predicted
that
total
number
cases
will
double
in
next
fifty
years.
It
an
expensive
procedure
to
discover
skin
types
early
stages.
Additionally,
survival
rate
reduces
as
progresses.
The
current
study
proposes
aseptic
approach
toward
lesion
detection,
classification,
and
segmentation
using
deep
learning
a
meta-heuristic
optimizer
called
Harris
Hawks
Optimization
Algorithm
(HHO).
utilized
manual
automatic
approaches.
used
when
dataset
has
no
masks
use
while
used,
U-Net
models,
build
adaptive
model.
HHO
achieve
optimization
hyperparameters
5
pre-trained
CNN
models
(i.e.,
VGG16,
VGG19,
DenseNet169,
DenseNet201,
MobileNet).
Two
collected
"Melanoma
Cancer
Dataset
10000
Images"
"Skin
ISIC"
dataset)
from
two
publically
available
sources.
For
segmentation,
best-reported
scores
0.15908,
91.95%,
0.08864,
0.04313,
0.02072,
0.20767
terms
loss,
accuracy,
Mean
Absolute
Error,
Squared
Logarithmic
Root
respectively.
dataset,
applied
experiments,
best
reported
overall
accuracy
97.08%
DenseNet169
96.06%
MobileNet
After
computing
results,
suggested
compared
with
9
related
studies.
Multimedia Tools and Applications,
Год журнала:
2023,
Номер
83(7), С. 19787 - 19815
Опубликована: Июль 28, 2023
Abstract
Skin
cancer
is
the
most
common
form
of
cancer.
It
predicted
that
total
number
cases
will
double
in
next
fifty
years.
an
expensive
procedure
to
discover
skin
types
early
stages.
Additionally,
survival
rate
reduces
as
progresses.
The
current
study
proposes
aseptic
approach
toward
lesion
detection,
classification,
and
segmentation
using
deep
learning
Harris
Hawks
Optimization
Algorithm
(HHO).
utilizes
manual
automatic
approaches.
used
when
dataset
has
no
masks
use
while
used,
U-Net
models,
build
adaptive
model.
meta-heuristic
HHO
optimizer
utilized
achieve
optimization
hyperparameters
5
pre-trained
CNN
namely
VGG16,
VGG19,
DenseNet169,
DenseNet201,
MobileNet.
Two
datasets
are
"Melanoma
Cancer
Dataset
10000
Images"
"Skin
ISIC"
from
two
publicly
available
sources
for
variety
purpose.
For
segmentation,
best-reported
scores
0.15908,
91.95%,
0.08864,
0.04313,
0.02072,
0.20767
terms
loss,
accuracy,
Mean
Absolute
Error,
Squared
Logarithmic
Root
respectively.
dataset,
applied
experiments,
best
reported
97.08%,
98.50%,
95.38%,
98.65%,
96.92%
overall
precision,
sensitivity,
specificity,
F1-score,
respectively
by
DenseNet169
96.06%,
83.05%,
81.05%,
97.93%,
82.03%
MobileNet
After
computing
results,
suggested
compared
with
9
related
studies.
results
comparison
proves
efficiency
proposed
framework.
Scientific Reports,
Год журнала:
2023,
Номер
13(1)
Опубликована: Март 1, 2023
Abstract
Several
artificial
intelligence
algorithms
have
been
developed
for
COVID-19-related
topics.
One
that
has
common
is
the
COVID-19
diagnosis
using
chest
X-rays,
where
eagerness
to
obtain
early
results
triggered
construction
of
a
series
datasets
bias
management
not
thorough
from
point
view
patient
information,
capture
conditions,
class
imbalance,
and
careless
mixtures
multiple
datasets.
This
paper
analyses
19
X-ray
images,
identifying
potential
biases.
Moreover,
computational
experiments
were
conducted
one
most
popular
in
this
domain,
which
obtains
96.19%
classification
accuracy
on
complete
dataset.
Nevertheless,
when
evaluated
with
ethical
tool
Aequitas,
it
fails
all
metrics.
Ethical
tools
enhanced
some
distribution
image
quality
considerations
are
keys
developing
or
choosing
dataset
fewer
issues.
We
aim
provide
broad
research
problems,
tools,
suggestions
future
developments
applications
images.
Healthcare,
Год журнала:
2023,
Номер
11(17), С. 2388 - 2388
Опубликована: Авг. 24, 2023
The
emergence
of
the
COVID-19
pandemic
in
Wuhan
2019
led
to
discovery
a
novel
coronavirus.
World
Health
Organization
(WHO)
designated
it
as
global
on
11
March
2020
due
its
rapid
and
widespread
transmission.
Its
impact
has
had
profound
implications,
particularly
realm
public
health.
Extensive
scientific
endeavors
have
been
directed
towards
devising
effective
treatment
strategies
vaccines.
Within
healthcare
medical
imaging
domain,
application
artificial
intelligence
(AI)
brought
significant
advantages.
This
study
delves
into
peer-reviewed
research
articles
spanning
years
2022,
focusing
AI-driven
methodologies
for
analysis
screening
through
chest
CT
scan
data.
We
assess
efficacy
deep
learning
algorithms
facilitating
decision
making
processes.
Our
exploration
encompasses
various
facets,
including
data
collection,
systematic
contributions,
emerging
techniques,
encountered
challenges.
However,
comparison
outcomes
between
2022
proves
intricate
shifts
dataset
magnitudes
over
time.
initiatives
aimed
at
developing
AI-powered
tools
detection,
localization,
segmentation
cases
are
primarily
centered
educational
training
contexts.
deliberate
their
merits
constraints,
context
necessitating
cross-population
train/test
models.
encompassed
review
231
publications,
bolstered
by
meta-analysis
employing
search
keywords
(COVID-19
OR
Coronavirus)
AND
(deep
imaging)
both
PubMed
Central
Repository
Web
Science
platforms.
Neural Computing and Applications,
Год журнала:
2024,
Номер
36(22), С. 13381 - 13465
Опубликована: Апрель 20, 2024
Abstract
This
paper
proposes
a
hybrid
Modified
Coronavirus
Herd
Immunity
Aquila
Optimization
Algorithm
(MCHIAO)
that
compiles
the
Enhanced
Optimizer
(ECHIO)
algorithm
and
(AO).
As
one
of
competitive
human-based
optimization
algorithms,
(CHIO)
exceeds
some
other
biological-inspired
algorithms.
Compared
to
CHIO
showed
good
results.
However,
gets
confined
local
optima,
accuracy
large-scale
global
problems
is
decreased.
On
hand,
although
AO
has
significant
exploitation
capabilities,
its
exploration
capabilities
are
insufficient.
Subsequently,
novel
metaheuristic
optimizer,
(MCHIAO),
presented
overcome
these
restrictions
adapt
it
solve
feature
selection
challenges.
In
this
paper,
MCHIAO
proposed
with
three
main
enhancements
issues
reach
higher
optimal
results
which
cases
categorizing,
enhancing
new
genes’
value
equation
using
chaotic
system
as
inspired
by
behavior
coronavirus
generating
formula
switch
between
expanded
narrowed
exploitation.
demonstrates
it’s
worth
contra
ten
well-known
state-of-the-art
algorithms
(GOA,
MFO,
MPA,
GWO,
HHO,
SSA,
WOA,
IAO,
NOA,
NGO)
in
addition
CHIO.
Friedman
average
rank
Wilcoxon
statistical
analysis
(
p
-value)
conducted
on
all
testing
23
benchmark
functions.
test
well
29
CEC2017
Moreover,
tests
10
CEC2019
Six
real-world
used
validate
against
same
twelve
classical
functions,
including
24
unimodal
44
multimodal
respectively,
exploitative
explorative
evaluated.
The
significance
technique
for
functions
demonstrated
-values
calculated
rank-sum
test,
found
be
less
than
0.05.
Bioengineering,
Год журнала:
2024,
Номер
11(6), С. 629 - 629
Опубликована: Июнь 19, 2024
Prostate
cancer
is
a
significant
health
concern
with
high
mortality
rates
and
substantial
economic
impact.
Early
detection
plays
crucial
role
in
improving
patient
outcomes.
This
study
introduces
non-invasive
computer-aided
diagnosis
(CAD)
system
that
leverages
intravoxel
incoherent
motion
(IVIM)
parameters
for
the
of
prostate
(PCa).
IVIM
imaging
enables
differentiation
water
molecule
diffusion
within
capillaries
outside
vessels,
offering
valuable
insights
into
tumor
characteristics.
The
proposed
approach
utilizes
two-step
segmentation
through
use
three
U-Net
architectures
extracting
tumor-containing
regions
interest
(ROIs)
from
segmented
images.
performance
CAD
thoroughly
evaluated,
considering
optimal
classifier
comparing
diagnostic
value
commonly
used
apparent
coefficient
(ADC).
results
demonstrate
combination
central
zone
(CZ)
peripheral
(PZ)
features
Random
Forest
Classifier
(RFC)
yields
best
performance.
achieves
an
accuracy
84.08%
balanced
82.60%.
showcases
sensitivity
(93.24%)
reasonable
specificity
(71.96%),
along
good
precision
(81.48%)
F1
score
(86.96%).
These
findings
highlight
effectiveness
accurately
segmenting
diagnosing
PCa.
represents
advancement
methods
early
PCa,
showcasing
potential
machine
learning
techniques.
developed
solution
has
to
revolutionize
PCa
diagnosis,
leading
improved
outcomes
reduced
healthcare
costs.
