Sensors,
Год журнала:
2022,
Номер
22(22), С. 8999 - 8999
Опубликована: Ноя. 21, 2022
Computer-aided
diagnosis
(CAD)
has
proved
to
be
an
effective
and
accurate
method
for
diagnostic
prediction
over
the
years.
This
article
focuses
on
development
of
automated
CAD
system
with
intent
perform
as
accurately
possible.
Deep
learning
methods
have
been
able
produce
impressive
results
medical
image
datasets.
study
employs
deep
in
conjunction
meta-heuristic
algorithms
supervised
machine-learning
diagnosis.
Pre-trained
convolutional
neural
networks
(CNNs)
or
auto-encoder
are
used
feature
extraction,
whereas
selection
is
performed
using
ant
colony
optimization
(ACO)
algorithm.
Ant
helps
search
best
optimal
features
while
reducing
amount
data.
Lastly,
(classification)
achieved
learnable
classifiers.
The
novel
framework
extraction
based
learning,
auto-encoder,
ACO.
performance
proposed
approach
evaluated
two
datasets:
chest
X-ray
(CXR)
magnetic
resonance
imaging
(MRI)
existence
COVID-19
brain
tumors.
Accuracy
main
measure
compare
existing
state-of-the-art
methods.
achieves
average
accuracy
99.61%
99.18%,
outperforming
all
other
diagnosing
presence
tumors,
respectively.
Based
results,
it
can
claimed
that
physicians
radiologists
confidently
utilize
patients
specific
Diabetes
is
a
widespread
illness
for
which
there
now
no
treatment.
Diabetes-related
flaws
cost
our
nation
lot
to
treat
each
year,
as
projected
in
the
therapy,
so
it's
crucial
predict
patients'
conditions
with
greater
precision.
Accurate
and
reliable
methodologies
should
be
utilised
make
predictions
high
level
of
accuracy
reliability.
Utilizing
neural
networks
other
artificial
intelligence
systems
one
these
techniques.
Given
statistical
models
like
logistic
regression
model,
new
combination
that
has
least
amount
error
highest
degree
dependability
examined
this
study.
The
numerical
results
produced,
When
compared
network
approaches,
acceptable
were
obtained
after
approach's
effectiveness
assessed
on
basis
aforementioned
recommendation
various
experiences,
comparison.
performance
standards
used
study
hybrid
network's
use
training
lower
function
are.
diabetes
prediction
using
supervised
learning
algorithms
presented
publication.
Data
from
250
diabetic
patients,
ranging
age
25
78,
train
network.
Regression
analysis
further
examine
how
method
performs.
To
confirm
an
accurate
forecast,
most
effective
algorithm's
established.
Sensors,
Год журнала:
2021,
Номер
21(11), С. 3925 - 3925
Опубликована: Июнь 7, 2021
In
this
paper,
a
novel
medical
image
encryption
method
based
on
multi-mode
synchronization
of
hyper-chaotic
systems
is
presented.
The
great
significance
in
secure
communication
tasks
such
as
images.
Multi-mode
and
highly
complex
issue,
especially
if
there
uncertainty
disturbance.
work,
an
adaptive-robust
controller
designed
for
multimode
synchronized
chaotic
with
variable
unknown
parameters,
despite
the
bounded
disturbance
known
function
two
modes.
first
case,
it
main
system
some
response
systems,
second
circular
synchronization.
Using
theorems
proved
that
methods
are
equivalent.
Our
results
show
that,
we
able
to
obtain
convergence
error
parameter
estimation
zero
using
Lyapunov’s
method.
new
laws
update
time-varying
estimating
bounds
proposed
stability
guaranteed.
To
assess
performance
method,
various
statistical
analyzes
were
carried
out
encrypted
images
standard
benchmark
effective
technique
telemedicine
application.
Diagnostics,
Год журнала:
2022,
Номер
12(8), С. 1853 - 1853
Опубликована: Июль 31, 2022
Coronavirus
disease
(COVID-19)
has
had
a
significant
impact
on
global
health
since
the
start
of
pandemic
in
2019.
As
June
2022,
over
539
million
cases
have
been
confirmed
worldwide
with
6.3
deaths
as
result.
Artificial
Intelligence
(AI)
solutions
such
machine
learning
and
deep
played
major
part
this
for
diagnosis
treatment
COVID-19.
In
research,
we
review
these
modern
tools
deployed
to
solve
variety
complex
problems.
We
explore
research
that
focused
analyzing
medical
images
using
AI
models
identification,
classification,
tissue
segmentation
disease.
also
prognostic
were
developed
predict
outcomes
optimize
allocation
scarce
resources.
Longitudinal
studies
conducted
better
understand
COVID-19
its
effects
patients
period
time.
This
comprehensive
different
methods
modeling
efforts
will
shed
light
role
what
path
it
intends
take
fight
against
Sensors,
Год журнала:
2022,
Номер
22(22), С. 8999 - 8999
Опубликована: Ноя. 21, 2022
Computer-aided
diagnosis
(CAD)
has
proved
to
be
an
effective
and
accurate
method
for
diagnostic
prediction
over
the
years.
This
article
focuses
on
development
of
automated
CAD
system
with
intent
perform
as
accurately
possible.
Deep
learning
methods
have
been
able
produce
impressive
results
medical
image
datasets.
study
employs
deep
in
conjunction
meta-heuristic
algorithms
supervised
machine-learning
diagnosis.
Pre-trained
convolutional
neural
networks
(CNNs)
or
auto-encoder
are
used
feature
extraction,
whereas
selection
is
performed
using
ant
colony
optimization
(ACO)
algorithm.
Ant
helps
search
best
optimal
features
while
reducing
amount
data.
Lastly,
(classification)
achieved
learnable
classifiers.
The
novel
framework
extraction
based
learning,
auto-encoder,
ACO.
performance
proposed
approach
evaluated
two
datasets:
chest
X-ray
(CXR)
magnetic
resonance
imaging
(MRI)
existence
COVID-19
brain
tumors.
Accuracy
main
measure
compare
existing
state-of-the-art
methods.
achieves
average
accuracy
99.61%
99.18%,
outperforming
all
other
diagnosing
presence
tumors,
respectively.
Based
results,
it
can
claimed
that
physicians
radiologists
confidently
utilize
patients
specific
