Journal of X-Ray Science and Technology,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 26, 2025
Background:
Chest
X-rays
are
an
essential
diagnostic
tool
for
identifying
chest
disorders
because
of
its
high
sensitivity
in
detecting
pathological
anomalies
the
lungs.
Classification
models
based
on
conventional
Convolutional
Neural
Networks
(CNNs)
adversely
affected
due
to
their
localization
bias.
Objective:
In
this
paper,
a
new
Multi-Axis
Transformer
U-Net
with
Class
Balanced
Ensemble
(MaxTU-CBE)
is
proposed
improve
multi-label
classification
performance.
Methods:
This
may
be
first
attempt
simultaneously
integrate
benefits
hierarchical
into
encoder
and
decoder
traditional
U-shaped
structure
improving
semantic
segmentation
superiority
lung
image.
Results:
A
key
element
MaxTU-CBE
Contextual
Fusion
Engine
(CFE),
which
uses
self-attention
mechanism
efficiently
create
global
interdependence
between
features
various
scales.
Also,
deep
CNN
incorporate
ensemble
learning
address
issue
class
unbalanced
learning.
Conclusions:
According
experimental
findings,
our
suggested
outperforms
competing
BiDLSTM
classifier
by
1.42%
CBIR-CSNN
techniques
5.2%
Diagnostics,
Journal Year:
2023,
Volume and Issue:
13(2), P. 262 - 262
Published: Jan. 10, 2023
Melanoma
is
known
worldwide
as
a
malignant
tumor
and
the
fastest-growing
skin
cancer
type.
It
very
life-threatening
disease
with
high
mortality
rate.
Automatic
melanoma
detection
improves
early
of
survival
In
accordance
this
purpose,
we
presented
multi-task
learning
approach
based
on
recognition
dermoscopy
images.
Firstly,
an
effective
pre-processing
max
pooling,
contrast,
shape
filters
used
to
eliminate
hair
details
perform
image
enhancement
operations.
Next,
lesion
region
was
segmented
VGGNet
model-based
FCN
Layer
architecture
using
enhanced
Later,
cropping
process
performed
for
detected
lesions.
Then,
cropped
images
were
converted
input
size
classifier
model
deep
super-resolution
neural
network
approach,
decrease
in
resolution
minimized.
Finally,
pre-trained
convolutional
networks
developed
classification.
We
International
Skin
Imaging
Collaboration,
publicly
available
dermoscopic
dataset
experimental
studies.
While
performance
measures
accuracy,
specificity,
precision,
sensitivity,
obtained
segmentation
region,
produced
at
rates
96.99%,
92.53%,
97.65%,
98.41%,
respectively,
achieved
classification
97.73%,
99.83%,
95.67%,
respectively.
Frontiers in Medicine,
Journal Year:
2022,
Volume and Issue:
9
Published: June 10, 2022
As
the
COVID-19
pandemic
devastates
globally,
use
of
chest
X-ray
(CXR)
imaging
as
a
complimentary
screening
strategy
to
RT-PCR
testing
continues
grow
given
its
routine
clinical
for
respiratory
complaint.
part
COVID-Net
open
source
initiative,
we
introduce
CXR-2,
an
enhanced
deep
convolutional
neural
network
design
detection
from
CXR
images
built
using
greater
quantity
and
diversity
patients
than
original
COVID-Net.
We
also
new
benchmark
dataset
composed
19,203
multinational
cohort
16,656
at
least
51
countries,
making
it
largest,
most
diverse
in
access
form.
The
CXR-2
achieves
sensitivity
positive
predictive
value
95.5
97.0%,
respectively,
was
audited
transparent
responsible
manner.
Explainability-driven
performance
validation
used
during
auditing
gain
deeper
insights
decision-making
behavior
ensure
clinically
relevant
factors
are
leveraged
improving
trust
usage.
Radiologist
conducted,
where
select
cases
were
reviewed
reported
on
by
two
board-certified
radiologists
with
over
10
19
years
experience,
showed
that
critical
consistent
radiologist
interpretations.
Asian Journal of Research in Computer Science,
Journal Year:
2022,
Volume and Issue:
unknown, P. 28 - 47
Published: Feb. 15, 2022
Deep
learning
(DL)
is
a
kind
of
sophisticated
data
analysis
and
image
processing
technology,
with
good
results
great
potential.
DL
has
been
applied
to
many
different
fields,
it
also
being
the
agricultural
field.
This
paper
presents
wide-ranging
review
research
regards
how
agriculture.
The
analyzed
works
were
categorized
in
yield
prediction,
weed
detection,
disease
detection.
articles
presented
here
illustrate
benefits
agriculture
through
filtering
categorization.
Farm
management
systems
are
turning
into
real-time
AI-enabled
applications
that
give
in-depth
insights
suggestions
for
farmer's
decision
support
by
using
proper
utilization
sensor
data.
