Structural Concrete,
Год журнала:
2025,
Номер
unknown
Опубликована: Июнь 2, 2025
Abstract
Urbanization
and
population
growth
have
increased
the
existing
building
stock,
making
it
more
challenging
to
assess
seismic
safety
of
buildings
due
time
constraints,
a
lack
skilled
personnel,
high
economic
costs.
In
this
study,
rapid
visual
screening
method
(RVS)
was
utilized
prioritize
vulnerability
reinforced
concrete
(RC)
buildings.
Accordingly,
an
integrated
model
combining
deep
feature
residual
networks,
learning‐based
architecture
relying
on
blocks,
XGBoost
proposed.
Additionally,
five
most
influential
parameters
for
determining
were
identified
using
technique.
RVS
methods
used
collect
data
RC
following
earthquakes
in
Afyon,
Bingöl,
Van,
Kahramanmaraş,
resulting
dataset
372
structures.
The
model's
performance
evaluated
accuracy,
precision,
recall,
F1‐score,
specificity,
AUC
metrics.
proposed
achieved
accuracy
rate
94.66%
Furthermore,
only
critical
features,
82.66%
obtained.
Sensitivity
analysis
performed
see
effect
model.
addition,
stability
tested
against
parameter
changes
or
possible
erroneous
inputs.
results
indicated
that
although
sensitive
changes,
its
predictions
remained
within
certain
limits
showed
stable
behavior
errors.
BMC Medical Imaging,
Год журнала:
2024,
Номер
24(1)
Опубликована: Фев. 1, 2024
Abstract
Background
Lung
diseases,
both
infectious
and
non-infectious,
are
the
most
prevalent
cause
of
mortality
overall
in
world.
Medical
research
has
identified
pneumonia,
lung
cancer,
Corona
Virus
Disease
2019
(COVID-19)
as
prominent
diseases
prioritized
over
others.
Imaging
modalities,
including
X-rays,
computer
tomography
(CT)
scans,
magnetic
resonance
imaging
(MRIs),
positron
emission
(PET)
others,
primarily
employed
medical
assessments
because
they
provide
computed
data
that
can
be
utilized
input
datasets
for
computer-assisted
diagnostic
systems.
used
to
develop
evaluate
machine
learning
(ML)
methods
analyze
predict
diseases.
Objective
This
review
analyzes
ML
paradigms,
modalities'
utilization,
recent
developments
Furthermore,
also
explores
various
available
publically
being
Methods
The
well-known
databases
academic
studies
have
been
subjected
peer
review,
namely
ScienceDirect,
arXiv,
IEEE
Xplore,
MDPI,
many
more,
were
search
relevant
articles.
Applied
keywords
combinations
procedures
with
primary
considerations
such
COVID-19,
ML,
convolutional
neural
networks
(CNNs),
transfer
learning,
ensemble
learning.
Results
finding
indicates
X-ray
preferred
detecting
while
CT
scan
predominantly
favored
cancer.
COVID-19
detection,
datasets.
analysis
reveals
X-rays
scans
surpassed
all
other
techniques.
It
observed
using
CNNs
yields
a
high
degree
accuracy
practicability
identifying
Transfer
complementary
techniques
facilitate
analysis.
is
metric
assessment.
Skin Research and Technology,
Год журнала:
2023,
Номер
29(11)
Опубликована: Ноя. 1, 2023
Particularly
within
the
Internet
of
Medical
Things
(IoMT)
context,
skin
lesion
analysis
is
critical
for
precise
diagnosis.
To
improve
accuracy
and
efficiency
analysis,
CAD
systems
play
a
crucial
role.
segment
classify
lesions
from
dermoscopy
images,
this
study
focuses
on
using
hybrid
deep
learning
techniques.
Medical & Biological Engineering & Computing,
Год журнала:
2024,
Номер
62(7), С. 2087 - 2100
Опубликована: Март 8, 2024
Abstract
The
pancreas
not
only
is
situated
in
a
complex
abdominal
background
but
also
surrounded
by
other
organs
and
adipose
tissue,
resulting
blurred
organ
boundaries.
Accurate
segmentation
of
pancreatic
tissue
crucial
for
computer-aided
diagnosis
systems,
as
it
can
be
used
surgical
planning,
navigation,
assessment
organs.
In
the
light
this,
current
paper
proposes
novel
Residual
Double
Asymmetric
Convolution
Network
(ResDAC-Net)
model.
Firstly,
newly
designed
ResDAC
blocks
are
to
highlight
features.
Secondly,
feature
fusion
between
adjacent
encoding
layers
fully
utilizes
low-level
deep-level
features
extracted
blocks.
Finally,
parallel
dilated
convolutions
employed
increase
receptive
field
capture
multiscale
spatial
information.
ResDAC-Net
highly
compatible
existing
state-of-the-art
models,
according
three
(out
four)
evaluation
metrics,
including
two
main
ones
performance
(i.e.,
DSC
Jaccard
index).
Graphical
abstract
Applied Soft Computing,
Год журнала:
2023,
Номер
144, С. 110500 - 110500
Опубликована: Июнь 19, 2023
Federated
learning
is
a
very
convenient
approach
for
scenarios
where
(i)
the
exchange
of
data
implies
privacy
concerns
and/or
(ii)
quick
reaction
needed.
In
smart
healthcare
systems,
both
aspects
are
usually
required.
this
paper,
we
work
on
first
scenario,
preserving
key
and,
consequently,
building
unique
and
massive
medical
image
set
by
fusing
different
sets
from
institutions
or
research
centers
(computation
nodes)
not
an
option.
We
propose
ensemble
federated
(EFL)
that
based
following
characteristics:
First,
each
computation
node
works
with
(but
same
type).
They
locally
apply
combining
eight
well-known
CNN
models
(densenet169,
mobilenetv2,
xception,
inceptionv3,
vgg16,
resnet50,
densenet121,
resnet152v2)
Chest
X-ray
images.
Second,
best
two
local
used
to
create
model
shared
central
node.
Third,
aggregated
obtain
global
model,
which
nodes
continue
new
iteration.
This
procedure
continues
until
there
no
changes
in
models.
have
performed
experiments
compare
our
centralized
ones
(with
without
approach)\color{black}.
The
results
conclude
proposal
outperforms
these
images
(achieving
accuracy
96.63\%)
offers
competitive
compared
other
proposals
literature.
