Healthcare,
Journal Year:
2022,
Volume and Issue:
10(12), P. 2395 - 2395
Published: Nov. 29, 2022
Incorporating
scientific
research
into
clinical
practice
via
informatics,
which
includes
genomics,
proteomics,
bioinformatics,
and
biostatistics,
improves
patients'
treatment.
Computational
pathology
is
a
growing
subspecialty
with
the
potential
to
integrate
whole
slide
images,
multi-omics
data,
health
informatics.
Pathology
laboratory
medicine
are
critical
diagnosing
cancer.
This
work
will
review
existing
computational
digital
methods
for
breast
cancer
diagnosis
special
focus
on
deep
learning.
The
paper
starts
by
reviewing
public
datasets
related
diagnosis.
Additionally,
learning
reviewed.
publicly
available
code
repositories
introduced
as
well.
closed
highlighting
challenges
future
works
learning-based
Soft Computing,
Journal Year:
2021,
Volume and Issue:
25(24), P. 15345 - 15362
Published: Aug. 24, 2021
The
new
coronavirus
disease
(COVID-19)
has
been
declared
a
pandemic
since
March
2020
by
the
World
Health
Organization.
It
consists
of
an
emerging
viral
infection
with
respiratory
tropism
that
could
develop
atypical
pneumonia.
Experts
emphasize
importance
early
detection
those
who
have
COVID-19
virus.
In
this
way,
patients
will
be
isolated
from
other
people
and
spread
virus
can
prevented.
For
reason,
it
become
area
interest
to
diagnosis
methods
ensure
rapid
treatment
process
prevent
spreading.
Since
standard
testing
system
is
time-consuming
not
available
for
everyone,
alternative
screening
techniques
urgent
need.
study,
approaches
used
in
based
on
deep
learning
(DL)
algorithms,
which
popular
recent
years,
comprehensively
discussed.
advantages
disadvantages
different
literature
are
examined
detail.
We
further
present
databases
major
future
challenges
DL-based
detection.
computed
tomography
chest
X-ray
images
gives
rich
representation
patient's
lung
less
allows
efficient
pneumonia
using
DL
algorithms.
first
step
preprocessing
these
remove
noise.
Next,
features
extracted
multiple
types
models
(pretrained
models,
generative
generic
neural
networks,
etc.).
Finally,
classification
performed
obtained
decide
whether
patient
infected
or
another
disease.
we
also
give
brief
review
latest
applications
cough
analysis
screen
human
mobility
estimation
limit
its
spread.
Sensors,
Journal Year:
2021,
Volume and Issue:
21(11), P. 3925 - 3925
Published: June 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.
Healthcare,
Journal Year:
2022,
Volume and Issue:
10(12), P. 2395 - 2395
Published: Nov. 29, 2022
Incorporating
scientific
research
into
clinical
practice
via
informatics,
which
includes
genomics,
proteomics,
bioinformatics,
and
biostatistics,
improves
patients'
treatment.
Computational
pathology
is
a
growing
subspecialty
with
the
potential
to
integrate
whole
slide
images,
multi-omics
data,
health
informatics.
Pathology
laboratory
medicine
are
critical
diagnosing
cancer.
This
work
will
review
existing
computational
digital
methods
for
breast
cancer
diagnosis
special
focus
on
deep
learning.
The
paper
starts
by
reviewing
public
datasets
related
diagnosis.
Additionally,
learning
reviewed.
publicly
available
code
repositories
introduced
as
well.
closed
highlighting
challenges
future
works
learning-based
