Results in Engineering,
Год журнала:
2024,
Номер
23, С. 102651 - 102651
Опубликована: Авг. 2, 2024
Ovarian
cancer,
a
significant
threat
to
women's
health,
demands
innovative
diagnostic
approaches.
This
paper
introduces
groundbreaking
Computer-Aided
Diagnosis
(CAD)
framework
for
the
classification
of
ovarian
integrating
Vision
Transformer
(ViT)
models
and
Local
Interpretable
Model-agnostic
Explanations
(LIME).
ViT
models,
including
ViT-Base-P16-224-In21K,
ViT-Base-P16-224,
ViT-Base-P32-384,
ViT-Large-P32-384,
exhibit
exceptional
accuracy,
precision,
recall,
overall
robust
performance
across
diverse
evaluation
metrics.
The
incorporation
stacked
model
further
enhances
performance.
Experimental
results,
conducted
on
UBC-OCEAN
training
testing
datasets,
highlight
proficiency
in
accurately
classifying
cancer
subtypes
based
histopathological
images.
ViT-Large-P32-384
stands
out
as
top
performer,
achieving
98.79%
accuracy
during
97.37%
testing.
Visualizations,
Receiver
Operating
Characteristic
(ROC)
curves
(LIME),
provide
insights
into
discriminative
capabilities
enhance
interpretability.
proposed
CAD
represents
advancement
diagnostics,
offering
promising
avenue
accurate
transparent
multi-class
Sensors,
Год журнала:
2023,
Номер
23(4), С. 2293 - 2293
Опубликована: Фев. 18, 2023
Parkinson’s
Disease
(PD)
is
one
of
the
most
common
non-curable
neurodegenerative
diseases.
Diagnosis
achieved
clinically
on
basis
different
symptoms
with
considerable
delays
from
onset
processes
in
central
nervous
system.
In
this
study,
we
investigated
early
and
full-blown
PD
patients
based
analysis
their
voice
characteristics
aid
commonly
employed
machine
learning
(ML)
techniques.
A
custom
dataset
was
made
hi-fi
quality
recordings
vocal
tasks
gathered
Italian
healthy
control
subjects
patients,
divided
into
diagnosed,
off-medication
hand,
mid-advanced
treated
L-Dopa
other.
Following
current
state-of-the-art,
several
ML
pipelines
were
compared
usingdifferent
feature
selection
classification
algorithms,
deep
also
explored
a
CNN
architecture.
Results
show
how
feature-based
achieve
comparable
results
terms
classification,
KNN,
SVM
naïve
Bayes
classifiers
performing
similarly,
slight
edge
for
KNN.
Much
more
evident
predominance
CFS
as
best
selector.
The
selected
features
act
relevant
biomarkers
capable
differentiating
subjects,
untreated
patients.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Янв. 8, 2024
Abstract
The
proposed
AI-based
diagnostic
system
aims
to
predict
the
respiratory
support
required
for
COVID-19
patients
by
analyzing
correlation
between
lesions
and
level
of
provided
patients.
Computed
tomography
(CT)
imaging
will
be
used
analyze
three
levels
received
patient:
Level
0
(minimum
support),
1
(non-invasive
such
as
soft
oxygen),
2
(invasive
mechanical
ventilation).
begin
segmenting
from
CT
images
creating
an
appearance
model
each
lesion
using
a
2D,
rotation-invariant,
Markov–Gibbs
random
field
(MGRF)
model.
Three
MGRF-based
models
created,
one
support.
This
suggests
that
able
differentiate
different
severity
in
decide
patient
neural
network-based
fusion
system,
which
combines
estimates
Gibbs
energy
models.
were
assessed
307
COVID-19-infected
patients,
achieving
accuracy
$$97.72\%\pm
1.57$$
97.72%±1.57
,
sensitivity
$$97.76\%\pm
4.08$$
97.764.08
specificity
$$98.87\%\pm
2.09$$
98.872.09
indicating
high
prediction
accuracy.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Янв. 29, 2024
Abstract
The
increase
in
eye
disorders
among
older
individuals
has
raised
concerns,
necessitating
early
detection
through
regular
examinations.
Age-related
macular
degeneration
(AMD),
a
prevalent
condition
over
45,
is
leading
cause
of
vision
impairment
the
elderly.
This
paper
presents
comprehensive
computer-aided
diagnosis
(CAD)
framework
to
categorize
fundus
images
into
geographic
atrophy
(GA),
intermediate
AMD,
normal,
and
wet
AMD
categories.
crucial
for
precise
age-related
enabling
timely
intervention
personalized
treatment
strategies.
We
have
developed
novel
system
that
extracts
both
local
global
appearance
markers
from
images.
These
are
obtained
entire
retina
iso-regions
aligned
with
optical
disc.
Applying
weighted
majority
voting
on
best
classifiers
improves
performance,
resulting
an
accuracy
96.85%,
sensitivity
93.72%,
specificity
97.89%,
precision
93.86%,
F1
ROC
95.85%,
balanced
95.81%,
sum
95.38%.
not
only
achieves
high
but
also
provides
detailed
assessment
severity
each
retinal
region.
approach
ensures
final
aligns
physician’s
understanding
aiding
them
ongoing
follow-up
patients.
Neural Computing and Applications,
Год журнала:
2024,
Номер
36(20), С. 12185 - 12298
Опубликована: Апрель 20, 2024
Abstract
Harris
Hawks
optimization
(HHO)
algorithm
was
a
powerful
metaheuristic
for
solving
complex
problems.
However,
HHO
could
easily
fall
within
the
local
minimum.
In
this
paper,
we
proposed
an
improved
(IHHO)
different
engineering
tasks.
The
focused
on
random
location-based
habitats
during
exploration
phase
and
strategies
1,
3,
4
exploitation
phase.
modified
hawks
in
wild
would
change
their
perch
strategy
chasing
pattern
according
to
updates
both
phases.
To
avoid
being
stuck
solution,
values
were
generated
using
logarithms
exponentials
explore
new
regions
more
quickly
locations.
evaluate
performance
of
algorithm,
IHHO
compared
other
five
recent
algorithms
[grey
wolf
optimization,
BAT
teaching–learning-based
moth-flame
whale
algorithm]
as
well
three
modifications
(BHHO,
LogHHO,
MHHO).
These
optimizers
had
been
applied
benchmarks,
namely
standard
CEC2017,
CEC2019,
CEC2020,
52
benchmark
functions.
Moreover,
six
classical
real-world
problems
tested
against
prove
efficiency
algorithm.
numerical
results
showed
superiority
algorithms,
which
proved
visually
convergence
curves.
Friedman's
mean
rank
statistical
test
also
inducted
calculate
algorithms.
Friedman
indicated
that
ranked
first
HHO.
Discover Artificial Intelligence,
Год журнала:
2025,
Номер
5(1)
Опубликована: Март 12, 2025
Millions
of
people
worldwide
suffer
from
Parkinson's
disease
(PD),
a
neurodegenerative
disorder
marked
by
motor
symptoms
such
as
tremors,
bradykinesia,
and
stiffness.
Accurate
early
diagnosis
is
crucial
for
effective
management
treatment.
This
article
presents
novel
review
Machine
Learning
(ML)
Deep
(DL)
techniques
PD
detection
progression
monitoring,
offering
new
perspectives
integrating
diverse
data
sources.
We
examine
the
public
datasets
recently
used
in
studies,
including
audio
recordings,
gait
analysis,
medical
imaging.
discuss
preprocessing
methods
applied,
state-of-the-art
models
utilized,
their
performance.
Our
evaluation
included
different
algorithms
support
vector
machines
(SVM),
random
forests
(RF),
convolutional
neural
networks
(CNN).
These
have
shown
promising
results
with
accuracy
rates
exceeding
99%
some
studies
combining
analysis
particularly
showcases
effectiveness
symptom
Unified
Disease
Rating
Scale
(UPDRS),
monitoring
progression.
Medical
imaging,
enhanced
DL
techniques,
has
improved
identification
PD.
The
application
ML
research
offers
significant
potential
improving
diagnostic
accuracy.
However,
challenges
like
need
large
datasets,
privacy
concerns,
quality
healthcare
remain.
Additionally,
developing
explainable
AI
to
ensure
that
clinicians
can
trust
understand
models.
highlights
these
key
must
be
addressed
enhance
robustness
applicability
diagnosis,
setting
groundwork
future
overcome
obstacles.