Pneumonia
is
a
serious
respiratory
infection
that
leads
to
lung
inflammation
and
the
accumulation
of
fluid
in
air
sacs.
Prompt
precise
diagnosis
plays
pivotal
role
ensuring
timely
treatment
enhancing
patient
outcomes.
Machine
learning
(ML)
techniques
have
proven
be
valuable
tools
for
pneumonia
detection,
offering
automated
efficient
analysis
medical
images,
particularly
chest
X-rays.
The
objective
this
research
assess
contrast
efficacy
two
disparate
methodologies
employing
range
classification
algorithm
seamlessly
integrated
with
convolutional
neural
networks
(CNNs)
identification
dataset
used
study
combination
COVID-19
Radiography
database
X-ray
images
obtained
from
Kaggle.
In
first
approach,
effectiveness
CNN-SVM,
CNN-LR,
CNN-KSVM
explored,
while
second
approach
involves
CNN-RF,
CNN-KNN,
CNN-DT,
CNN-NB.
evaluation
these
different
algorithms
reveals
CNN-SVM
achieves
an
accuracy
95%,
making
it
highest-performing
method
among
seven
employed
research.
