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
PeerJ Computer Science,
Год журнала:
2025,
Номер
11, С. e2686 - e2686
Опубликована: Фев. 17, 2025
We
introduce
a
sophisticated
deep-learning
model
designed
for
the
early
detection
of
COVID-19
and
pneumonia.
The
employs
convolutional
neural
network-integrated
with
atrous
spatial
pyramid
pooling.
pooling
mechanism
enhances
network
model's
ability
to
capture
fine
large-scale
features,
optimizing
accuracy
in
chest
X-ray
images.
This
improvement,
along
transfer
learning,
significantly
overall
performance.
By
utilizing
data
augmentation
address
scarcity
available
images,
our
pooling-enhanced
achieved
validation
98.66%
83.75%
pneumonia,
which
beats
results
other
state
art
approaches
(the
metrics
used
evaluation
were
accuracy,
precision,
F1-score,
recall,
specificity,
area
under
curve).
multi-branch
architecture
facilitates
more
accurate
adaptable
disease
prediction,
thereby
increasing
diagnostic
precision
robustness.
approach
offers
potential
faster
reliable
diagnoses
chest-related
conditions.
