2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON),
Journal Year:
2023,
Volume and Issue:
unknown, P. 1789 - 1795
Published: Dec. 1, 2023
The
study
has
been
conducted
to
understand
the
effectiveness
of
CNN
in
case
"COVID-19"
detection
by
using
all
X-ray
images
chest.
This
helps
health
sector
facilitate
medical
performance
and
conduct
analytical
procedure
ML.
aim
objective
have
properly
mentioned
first
segment
study.
issues
also
identified
second
chapter
previous
work
related
methodology
illustrated
methods
that
applied
implement
project.
resulting
themes
with
proper
evaluation
a
comprehensive
way.
last
represented
conclusion
recommendations
future
can
be
implemented
later.
Diagnostics,
Journal Year:
2023,
Volume and Issue:
13(18), P. 2939 - 2939
Published: Sept. 13, 2023
Colon
cancer
is
the
third
most
common
type
worldwide
in
2020,
almost
two
million
cases
were
diagnosed.
As
a
result,
providing
new,
highly
accurate
techniques
detecting
colon
leads
to
early
and
successful
treatment
of
this
disease.
This
paper
aims
propose
heterogenic
stacking
deep
learning
model
predict
cancer.
Stacking
integrated
with
pretrained
convolutional
neural
network
(CNN)
models
metalearner
enhance
prediction
performance.
The
proposed
compared
VGG16,
InceptionV3,
Resnet50,
DenseNet121
using
different
evaluation
metrics.
Furthermore,
are
evaluated
LC25000
WCE
binary
muticlassified
image
datasets.
results
show
that
recorded
highest
performance
for
For
dataset,
stacked
accuracy,
recall,
precision,
F1
score
(100).
(98).
Stacking-SVM
achieved
performed
existing
(VGG16,
DenseNet121)
because
it
combines
output
multiple
single
trains
evaluates
produce
better
predictive
than
any
model.
Black-box
represented
explainable
AI
(XAI).
BioMedInformatics,
Journal Year:
2023,
Volume and Issue:
3(3), P. 691 - 713
Published: Sept. 1, 2023
Since
December
2019,
a
novel
coronavirus
disease
(COVID-19)
has
infected
millions
of
individuals.
This
paper
conducts
thorough
study
the
use
deep
learning
(DL)
and
federated
(FL)
approaches
to
COVID-19
screening.
To
begin,
an
evaluation
research
articles
published
between
1
January
2020
28
June
2023
is
presented,
considering
preferred
reporting
items
systematic
reviews
meta-analysis
(PRISMA)
guidelines.
The
review
compares
various
datasets
on
medical
imaging,
including
X-ray,
computed
tomography
(CT)
scans,
ultrasound
images,
in
terms
number
samples,
classes
datasets.
Following
that,
description
existing
DL
algorithms
applied
offered.
Additionally,
summary
recent
work
FL
for
screening
provided.
Efforts
improve
quality
models
are
comprehensively
reviewed
objectively
evaluated.
PeerJ Computer Science,
Journal Year:
2024,
Volume and Issue:
10, P. e2517 - e2517
Published: Dec. 24, 2024
The
global
spread
of
SARS-CoV-2
has
prompted
a
crucial
need
for
accurate
medical
diagnosis,
particularly
in
the
respiratory
system.
Current
diagnostic
methods
heavily
rely
on
imaging
techniques
like
CT
scans
and
X-rays,
but
identifying
these
images
proves
to
be
challenging
time-consuming.
In
this
context,
artificial
intelligence
(AI)
models,
specifically
deep
learning
(DL)
networks,
emerge
as
promising
solution
image
analysis.
This
article
provides
meticulous
comprehensive
review
imaging-based
diagnosis
using
up
May
2024.
starts
with
an
overview
covering
basic
steps
learning-based
data
sources,
pre-processing
methods,
taxonomy
techniques,
findings,
research
gaps
performance
evaluation.
We
also
focus
addressing
current
privacy
issues,
limitations,
challenges
realm
diagnosis.
According
taxonomy,
each
model
is
discussed,
encompassing
its
core
functionality
critical
assessment
suitability
detection.
A
comparative
analysis
included
by
summarizing
all
relevant
studies
provide
overall
visualization.
Considering
best
deep-learning
detection,
conducts
experiment
twelve
contemporary
techniques.
experimental
result
shows
that
MobileNetV3
outperforms
other
models
accuracy
98.11%.
Finally,
elaborates
explores
potential
future
directions
methodological
recommendations
advancement.
International Journal of Advanced Research in Science Communication and Technology,
Journal Year:
2024,
Volume and Issue:
unknown, P. 51 - 68
Published: May 8, 2024
The
coronavirus
disease
2019
(COVID-19)
pandemic
caused
by
severe
acute
respiratory
syndrome
2
(SARS-CoV-2)
has
significantly
impacted
global
health.
This
review
aims
to
provide
a
comprehensive
overview
of
the
signs,
symptoms,
diagnosis,
and
treatment
modalities
COVID-19.
clinical
presentation
COVID-19
varies
widely,
ranging
from
asymptomatic
or
mild
symptoms
distress
multiorgan
failure.
Common
include
fever,
cough,
fatigue,
dyspnea,
with
less
frequent
such
as
anosmia,
ageusia,
gastrointestinal
symptoms.
Diagnosis
primarily
relies
on
reverse
transcription-polymerase
chain
reaction
(RT-PCR)
testing
specimens.
However,
imaging
chest
X-ray
Antibody
Test
Antigen
test
in
especially
cases
atypical
presentations.
Treatment
strategies
supportive
care,
antiviral
therapy,
and,
cases,
other
intensive
care
measures.
development
distribution
vaccines
have
been
pivotal
controlling
spread
virus.
Despite
significant
progress
understanding
managing
COVID-19,
ongoing
research
is
crucial
refine
diagnostic
strategies,
develop
effective
therapies,
improve
patient
outcomes.
Antiviral
drugs,
remdesivir,
poxolovid,
molonupiravir,
widely
used
inhibit
viral
replication
reduce
severity
duration
Immunomodulators,
including
tocilizumab
target
specific
pathways
involved
hyperinflammatory
response
seen
Monoclonal
antibodies,
casirivimab/imdevimab
sotrovimab,
employed
for
passive
immunization
neutralize
virus
risk
progression
