BMC Medical Imaging,
Journal Year:
2024,
Volume and Issue:
24(1)
Published: July 30, 2024
Abstract
Breast
cancer
is
a
prevalent
disease
and
the
second
leading
cause
of
death
in
women
globally.
Various
imaging
techniques,
including
mammography,
ultrasonography,
X-ray,
magnetic
resonance,
are
employed
for
detection.
Thermography
shows
significant
promise
early
breast
detection,
offering
advantages
such
as
being
non-ionizing,
non-invasive,
cost-effective,
providing
real-time
results.
Medical
image
segmentation
crucial
analysis,
this
study
introduces
thermographic
algorithm
using
improved
Black
Widow
Optimization
Algorithm
(IBWOA).
While
standard
BWOA
effective
complex
optimization
problems,
it
has
issues
with
stagnation
balancing
exploration
exploitation.
The
proposed
method
enhances
Levy
flights
improves
exploitation
quasi-opposition-based
learning.
Comparing
IBWOA
other
algorithms
like
Harris
Hawks
(HHO),
Linear
Success-History
based
Adaptive
Differential
Evolution
(LSHADE),
whale
(WOA),
sine
cosine
(SCA),
black
widow
(BWO)
otsu
Kapur's
entropy
method.
Results
show
delivers
superior
performance
both
qualitative
quantitative
analyses
visual
inspection
metrics
fitness
value,
threshold
values,
peak
signal-to-noise
ratio
(PSNR),
structural
similarity
index
measure
(SSIM),
feature
(FSIM).
Experimental
results
demonstrate
outperformance
IBWOA,
validating
its
effectiveness
superiority.
Computers, materials & continua/Computers, materials & continua (Print),
Journal Year:
2021,
Volume and Issue:
71(2), P. 3445 - 3462
Published: Dec. 7, 2021
This
paper
provides
a
new
optimization
algorithm
named
as
tunicate
swarm
naked
mole-rat
(TSNMRA)
which
uses
hybridization
concept
of
(TSA)
and
(NMRA).
newly
developed
the
characteristics
both
algorithms
(TSA
NMRA)
enhance
exploration
abilities
NMRA.
Apart
from
concept,
important
parameter
NMRA
such
mating
factor
is
made
to
be
self-adaptive
with
help
simulated
annealing
mutation
operator
there
no
need
define
its
value
manually.
For
evaluating
working
capabilities
proposed
TSNMRA,
it
tested
for
100-digit
challenge
(CEC
2019)
test
problems
real
multi-level
image
segmentation
problem.
From
results
obtained
CEC
2019
problems,
can
seen
that
TSNMRA
performs
well
compared
original
TSA
In
case
problem,
comparison
performed
multi-threshold
electro
magnetism-like
(MTEMO),
particle
(PSO),
genetic
(GA),
bacterial
foraging
(BF)
found
superior
TSNMRA.
Journal of Intelligent & Robotic Systems,
Journal Year:
2022,
Volume and Issue:
105(2)
Published: May 20, 2022
Abstract
The
urban
air
mobility
market
is
expected
to
grow
constantly
due
the
increased
interest
in
new
forms
of
transportation.
Managing
aerial
vehicles
fleets,
dependent
on
rising
technologies
such
as
artificial
intelligence
and
automated
ground
control
stations,
will
require
a
solid
uninterrupted
connection
complete
their
trajectories.
A
path
planner
based
evolutionary
algorithms
find
most
suitable
route
has
been
previously
proposed
by
authors.
Herein,
we
propose
using
particle
swarm
hybrid
optimisation
instead
this
work.
goal
speeding
planning
process
reducing
computational
costs
achieved
direct
search
algorithms.
This
improved
efficiently
explores
space
proposes
trajectory
according
its
predetermined
goals:
maximum
air-to-ground
quality,
availability,
flight
time.
proposal
tested
different
situations,
including
diverse
terrain
conditions
for
various
channel
behaviours
no-fly
zones.
The Imaging Science Journal,
Journal Year:
2022,
Volume and Issue:
70(2), P. 75 - 86
Published: Feb. 17, 2022
Segmentation
of
wound
images
is
important
for
efficient
treatment
so
that
appropriate
methods
can
be
recommended
quickly.
Wound
measurement,
subjective
an
overall
assessment.
The
establishment
a
high-performance
automatic
segmentation
system
great
importance
care.
use
machine
learning
will
make
performing
with
high
performance
possible.
Great
success
achieved
deep
learning,
which
sub-branch
and
has
been
used
in
the
analysis
recently
(classification,
segmentation,
etc.).
In
this
study,
pressure
was
discussed
different
encoder-decoder
based
models.
All
are
implemented
on
Medetec
image
dataset.
experiments,
FCN,
PSP,
UNet,
SegNet
DeepLabV3
architectures
were
five-fold
cross-validation.
Performances
models
measured
experiments
it
demonstrated
most
successful
architecture
MobileNet-UNet
99.67%
accuracy.
BMC Medical Imaging,
Journal Year:
2024,
Volume and Issue:
24(1)
Published: July 30, 2024
Abstract
Breast
cancer
is
a
prevalent
disease
and
the
second
leading
cause
of
death
in
women
globally.
Various
imaging
techniques,
including
mammography,
ultrasonography,
X-ray,
magnetic
resonance,
are
employed
for
detection.
Thermography
shows
significant
promise
early
breast
detection,
offering
advantages
such
as
being
non-ionizing,
non-invasive,
cost-effective,
providing
real-time
results.
Medical
image
segmentation
crucial
analysis,
this
study
introduces
thermographic
algorithm
using
improved
Black
Widow
Optimization
Algorithm
(IBWOA).
While
standard
BWOA
effective
complex
optimization
problems,
it
has
issues
with
stagnation
balancing
exploration
exploitation.
The
proposed
method
enhances
Levy
flights
improves
exploitation
quasi-opposition-based
learning.
Comparing
IBWOA
other
algorithms
like
Harris
Hawks
(HHO),
Linear
Success-History
based
Adaptive
Differential
Evolution
(LSHADE),
whale
(WOA),
sine
cosine
(SCA),
black
widow
(BWO)
otsu
Kapur's
entropy
method.
Results
show
delivers
superior
performance
both
qualitative
quantitative
analyses
visual
inspection
metrics
fitness
value,
threshold
values,
peak
signal-to-noise
ratio
(PSNR),
structural
similarity
index
measure
(SSIM),
feature
(FSIM).
Experimental
results
demonstrate
outperformance
IBWOA,
validating
its
effectiveness
superiority.
