Communications Engineering,
Год журнала:
2025,
Номер
4(1)
Опубликована: Янв. 22, 2025
Vision
impairment
affects
nearly
2.2
billion
people
globally,
and
half
of
these
cases
could
be
prevented
with
early
diagnosis
intervention—underscoring
the
urgent
need
for
reliable
scalable
detection
methods
conditions
like
diabetic
retinopathy
age-related
macular
degeneration.
Here
we
propose
a
distributed
deep
learning
framework
that
integrates
self-supervised
domain-adaptive
federated
to
enhance
eye
diseases
from
optical
coherence
tomography
images.
We
employed
self-supervised,
mask-based
pre-training
strategy
develop
robust
foundation
encoder.
This
encoder
was
trained
on
seven
datasets,
compared
its
performance
under
local,
centralized,
settings.
Our
results
show
methods—both
centralized
federated—improved
area
curve
by
at
least
10%
local
models.
Additionally,
incorporating
domain
adaptation
into
further
boosted
generalization
across
different
populations
imaging
conditions.
approach
supports
collaborative
model
development
without
data
sharing,
providing
scalable,
privacy-preserving
solution
effective
retinal
disease
screening
in
diverse
clinical
Sina
Gholami
co-authors
use
improve
multi-class
classification
Their
preserves
privacy
ensuring
robust,
significant
gains.
Artificial Intelligence in Agriculture,
Год журнала:
2024,
Номер
12, С. 1 - 18
Опубликована: Март 11, 2024
Leaf
blight
spot
disease,
caused
by
bacteria
and
fungi,
poses
a
threat
to
plant
health,
leading
leaf
discoloration
diminished
agricultural
yield.
In
response,
we
present
MobileNetV3-based
classifier
designed
for
the
Jasmine
plant,
leveraging
lightweight
Convolutional
Neural
Networks
(CNNs)
accurately
identify
disease
stages.
The
model
integrates
depth-wise
convolution
layers
max
pool
enhanced
feature
extraction,
focusing
on
crucial
low-level
features
indicative
of
disease.
Through
preprocessing
techniques,
including
data
augmentation
with
Conditional
GAN
Particle
Swarm
Optimization
selection,
achieves
robust
performance.
Evaluation
curated
datasets
demonstrates
an
outstanding
97%
training
accuracy,
highlighting
its
efficacy.
Real-world
testing
diverse
conditions,
such
as
extreme
camera
angles
varied
lighting,
attests
model's
resilience,
yielding
test
accuracies
between
94%
96%.
dataset's
tailored
design
CNN-based
classification
ensures
result
reliability.
Importantly,
classification,
marked
fast
computation
time
reduced
size,
positions
it
efficient
solution
real-time
applications.
This
comprehensive
approach
underscores
proposed
classifier's
significance
in
addressing
challenges
commercial
crops.
Communications Engineering,
Год журнала:
2025,
Номер
4(1)
Опубликована: Янв. 22, 2025
Vision
impairment
affects
nearly
2.2
billion
people
globally,
and
half
of
these
cases
could
be
prevented
with
early
diagnosis
intervention—underscoring
the
urgent
need
for
reliable
scalable
detection
methods
conditions
like
diabetic
retinopathy
age-related
macular
degeneration.
Here
we
propose
a
distributed
deep
learning
framework
that
integrates
self-supervised
domain-adaptive
federated
to
enhance
eye
diseases
from
optical
coherence
tomography
images.
We
employed
self-supervised,
mask-based
pre-training
strategy
develop
robust
foundation
encoder.
This
encoder
was
trained
on
seven
datasets,
compared
its
performance
under
local,
centralized,
settings.
Our
results
show
methods—both
centralized
federated—improved
area
curve
by
at
least
10%
local
models.
Additionally,
incorporating
domain
adaptation
into
further
boosted
generalization
across
different
populations
imaging
conditions.
approach
supports
collaborative
model
development
without
data
sharing,
providing
scalable,
privacy-preserving
solution
effective
retinal
disease
screening
in
diverse
clinical
Sina
Gholami
co-authors
use
improve
multi-class
classification
Their
preserves
privacy
ensuring
robust,
significant
gains.