PLoS ONE,
Journal Year:
2024,
Volume and Issue:
19(12), P. e0312016 - e0312016
Published: Dec. 5, 2024
Diabetic
retinopathy
(DR)
is
a
prominent
reason
of
blindness
globally,
which
diagnostically
challenging
disease
owing
to
the
intricate
process
its
development
and
human
eye’s
complexity,
consists
nearly
forty
connected
components
like
retina,
iris,
optic
nerve,
so
on.
This
study
proposes
novel
approach
identification
DR
employing
methods
such
as
synthetic
data
generation,
K-
Means
Clustering-Based
Binary
Grey
Wolf
Optimizer
(KCBGWO),
Fully
Convolutional
Encoder-Decoder
Networks
(FCEDN).
achieved
using
Generative
Adversarial
(GANs)
generate
high-quality
transfer
learning
for
accurate
feature
extraction
classification,
integrating
these
with
Extreme
Learning
Machines
(ELM).
The
substantial
evaluation
plan
we
have
provided
on
IDRiD
dataset
gives
exceptional
outcomes,
where
our
proposed
model
99.87%
accuracy
99.33%
sensitivity,
while
specificity
99.
78%.
why
outcomes
presented
can
be
viewed
promising
in
terms
further
diagnosis,
well
creating
new
reference
point
within
framework
medical
image
analysis
providing
more
effective
timely
treatments.
PLoS ONE,
Journal Year:
2025,
Volume and Issue:
20(3), P. e0318219 - e0318219
Published: March 19, 2025
Lung
cancer
(LC)
is
a
leading
cause
of
cancer-related
fatalities
worldwide,
underscoring
the
urgency
early
detection
for
improved
patient
outcomes.
The
main
objective
this
research
to
harness
noble
strategies
artificial
intelligence
identifying
and
classifying
lung
cancers
more
precisely
from
CT
scan
images
at
stage.
This
study
introduces
novel
method,
which
was
mainly
focused
on
Convolutional
Neural
Networks
(CNN)
later
customized
binary
multiclass
classification
utilizing
publicly
available
dataset
chest
cancer.
contribution
lies
in
its
use
hybrid
CNN-SVD
(Singular
Value
Decomposition)
method
robust
voting
ensemble
approach,
results
superior
accuracy
effectiveness
mitigating
potential
errors.
By
employing
contrast-limited
adaptive
histogram
equalization
(CLAHE),
contrast-enhanced
were
generated
with
minimal
noise
prominent
distinctive
features.
Subsequently,
CNN-SVD-Ensemble
model
implemented
extract
important
features
reduce
dimensionality.
extracted
then
processed
by
set
ML
algorithms
along
approach.
Additionally,
Gradient-weighted
Class
Activation
Mapping
(Grad-CAM)
integrated
as
an
explainable
AI
(XAI)
technique
enhancing
transparency
highlighting
key
influencing
regions
scans,
interpretability
ensured
reliable
trustworthy
clinical
applications.
offered
state-of-the-art
results,
achieved
remarkable
performance
metrics
accuracy,
AUC,
precision,
recall,
F1
score,
Cohen’s
Kappa
Matthews
Correlation
Coefficient
(MCC)
99.49%,
99.73%,
100%,
99%,
99.15%
99.16%,
respectively,
addressing
prior
gaps
setting
new
benchmark
field.
Furthermore,
class
classification,
all
indicators
attained
perfect
score
100%.
robustness
suggested
approach
impactful
insights
medical
field,
thus
improving
existing
knowledge
stage
future
innovations.
