In
order
to
improve
diagnostic
precision,
this
study
offers
an
original
framework
for
multimodal
health
image
fusion
that
makes
use
of
cloud-based
deep
learning.
A
descriptive
design
is
used
with
additional
information
gathering,
utilizing
approach
deductive
along
interpretivist
perspective.
The
convolutional
neural
network-based
suggested
model
assessed
in
terms
its
scalability,
effectiveness,
and
stored
the
cloud
computational
effectiveness.
When
results
are
compared
current
techniques,
they
demonstrate
higher
precision.
model's
possible
consequences
on
healthcare
highlighted
by
interpretation
clinical
utility.
Limitations
addressed
through
critical
analysis,
suggestions
include
enhancing
model,
investigating
edge
computing,
taking
ethical
issues
into
account.
Subsequent
efforts
ought
concentrate
refining
growing
dataset,
guaranteeing
interpretability.
Sir Syed University Research Journal of Engineering & Technology,
Journal Year:
2024,
Volume and Issue:
14(2), P. 55 - 62
Published: Dec. 27, 2024
Medical
imaging
is
a
critical
tool
for
diagnosing
and
treating
various
diseases
such
as
Chronic
Obstructive
Pulmonary
Disease
(COPD),
tuberculosis,
lung
cancer,
Coronavirus.
Techniques
X-rays,
Computed
Tomography
(CT),
Magnetic
Resonance
Imaging
(MRI),
Positron
Emission
(PET)
play
essential
roles
in
identifying
the
physical
functional
aspects
of
lungs.
Manual
segmentation
by
radiologists,
while
adjustable,
time-consuming
subject
to
variability.
Consequently,
automated
methods
utilizing
Machine
Learning
(ML)
Deep
(DL)
have
emerged
alternatives.
This
review
highlights
advancements
segmentation,
focusing
on
traditional
ML
state-of-the-art
DL
approaches,
particularly
Convolutional
Neural
Networks
(CNNs)
Generative
Adversarial
(GANs).
While
these
techniques
hold
great
promise,
challenges
remain,
need
annotated
datasets,
computational
demands,
integration
into
clinical
workflows.
paper
explores
current
applications,
identifies
challenges,
outlines
future
opportunities
improving
precision
efficiency
through
interdisciplinary
collaboration
medical
imaging,
computer
science,
practice.
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering,
Journal Year:
2023,
Volume and Issue:
14(1), P. 358 - 358
Published: Nov. 14, 2023
<span
lang="EN-IN">In
recent
years,
information
technology
has
vastly
improved.
The
quality
of
the
image
been
degraded
by
noise,
which
defeats
purpose
noisy
images.
major
this
paper
is
to
find
out
filters
provide
a
better
outcome
while
preprocessing
medical
images
using
computer
tomography
scans.
remove
noise
from
any
images,
whether
they
are
real-time
datasets
or
online
datasets.
To
enhance
an
for
preprocessing,
I
have
compared
various
filters;
these
already
available,
but
identify
best
filter.
different
parameters
and
finally
found
that
modified
bilateral
filtering
provided
result.
removed
filter,
clarity
not
changed
when
We
discussed
advantages
drawbacks
each
approach.
effectiveness
peak
signal-to-noise
ratio,
structural
similarity
index,
mean
square
error.
An
enhanced
processing
analysis
techniques
can
improve
accuracy
diagnosis,
facilitating
timely
treatment
ultimately
improving
patient
outcomes.</span>
Journal of Intelligent & Fuzzy Systems,
Journal Year:
2023,
Volume and Issue:
unknown, P. 1 - 15
Published: Dec. 6, 2023
Lung
cancer
is
one
of
the
leading
causes
mortality
from
cancer.
a
kind
malignant
lung
tumor
characterized
by
uncontrolled
cell
proliferation
in
tissues.
Even
though
CT
scans
are
most
often
used
imaging
technology
medicine,
clinicians
find
it
challenging
to
interpret
and
diagnose
scan
pictures.
As
result,
computer-aided
diagnostics
can
assist
precisely
identifying
cells.
Many
approaches
were
explored
applied,
including
image
processing
machine
learning.
A
comparison
various
classification
methodologies
will
enhancing
accuracy
detection
systems
that
employ
robust
segmentation
algorithms
presented
this
research.
This
research
proposed
enhance
existing
classification-basedmethodsof
human
with
optimization
techniques.
The
workflow
includes
initial
preprocessing
medical
images,
for
novel
hybrid
methodology
developed
combining
enhanced
k-means
clustering
random
forest
an
Artificial
neural
network
PSO
parameter
feature
optimization.
In
order
to
improve
diagnostic
precision,
this
study
offers
an
original
framework
for
multimodal
health
image
fusion
that
makes
use
of
cloud-based
deep
learning.
A
descriptive
design
is
used
with
additional
information
gathering,
utilizing
approach
deductive
along
interpretivist
perspective.
The
convolutional
neural
network-based
suggested
model
assessed
in
terms
its
scalability,
effectiveness,
and
stored
the
cloud
computational
effectiveness.
When
results
are
compared
current
techniques,
they
demonstrate
higher
precision.
model's
possible
consequences
on
healthcare
highlighted
by
interpretation
clinical
utility.
Limitations
addressed
through
critical
analysis,
suggestions
include
enhancing
model,
investigating
edge
computing,
taking
ethical
issues
into
account.
Subsequent
efforts
ought
concentrate
refining
growing
dataset,
guaranteeing
interpretability.
