ZooCNN: A Zero-Order Optimized Convolutional Neural Network for Pneumonia Classification Using Chest Radiographs
Saravana Kumar Ganesan,
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V. Parthasarathy,
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R. Santhosh
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et al.
Journal of Imaging,
Journal Year:
2025,
Volume and Issue:
11(1), P. 22 - 22
Published: Jan. 13, 2025
Pneumonia,
a
leading
cause
of
mortality
in
children
under
five,
is
usually
diagnosed
through
chest
X-ray
(CXR)
images
due
to
its
efficiency
and
cost-effectiveness.
However,
the
shortage
radiologists
Least
Developed
Countries
(LDCs)
emphasizes
need
for
automated
pneumonia
diagnostic
systems.
This
article
presents
Deep
Learning
model,
Zero-Order
Optimized
Convolutional
Neural
Network
(ZooCNN),
Optimization
(Zoo)-based
CNN
model
classifying
CXR
into
three
classes,
Normal
Lungs
(NL),
Bacterial
Pneumonia
(BP),
Viral
(VP);
this
utilizes
Adaptive
Synthetic
Sampling
(ADASYN)
approach
ensure
class
balance
Kaggle
Images
(Pneumonia)
dataset.
Conventional
models,
though
promising,
face
challenges
such
as
overfitting
have
high
computational
costs.
The
use
ZooPlatform
(ZooPT),
hyperparameter
finetuning
strategy,
on
baseline
finetunes
hyperparameters
provides
modified
architecture,
ZooCNN,
with
72%
reduction
weights.
was
trained,
tested,
validated
ZooCNN
achieved
an
accuracy
97.27%,
sensitivity
97.00%,
specificity
98.60%,
F1
score
97.03%.
results
were
compared
contemporary
models
highlight
efficacy
classification
(PC),
offering
potential
tool
aid
physicians
clinical
settings.
Language: Английский
Enhanced Multi-Model Deep Learning for Rapid and Precise Diagnosis of Pulmonary Diseases Using Chest X-Ray Imaging
Rahul Kumar,
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Cheng‐Tang Pan,
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Yimin Lin
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et al.
Diagnostics,
Journal Year:
2025,
Volume and Issue:
15(3), P. 248 - 248
Published: Jan. 22, 2025
Background:
The
global
burden
of
respiratory
diseases
such
as
influenza,
tuberculosis,
and
viral
pneumonia
necessitates
rapid,
accurate
diagnostic
tools
to
improve
healthcare
responses.
Current
methods,
including
RT-PCR
chest
radiography,
face
limitations
in
accuracy,
speed,
accessibility,
cost-effectiveness,
especially
resource-constrained
settings,
often
delaying
treatment
increasing
transmission.
Methods:
This
study
introduces
an
Enhanced
Multi-Model
Deep
Learning
(EMDL)
approach
address
these
challenges.
EMDL
integrates
ensemble
five
pre-trained
deep
learning
models
(VGG-16,
VGG-19,
ResNet,
AlexNet,
GoogleNet)
with
advanced
image
preprocessing
(histogram
equalization
contrast
enhancement)
a
novel
multi-stage
feature
selection
optimization
pipeline
(PCA,
SelectKBest,
Binary
Particle
Swarm
Optimization
(BPSO),
Grey
Wolf
(BGWO)).
Results:
Evaluated
on
two
independent
X-ray
datasets,
achieved
high
accuracy
the
multiclass
classification
pneumonia,
tuberculosis.
combined
enhancement
strategies
significantly
improved
precision
model
robustness.
Conclusions:
framework
provides
scalable
efficient
solution
for
accessible
pulmonary
disease
diagnosis,
potentially
improving
efficacy
patient
outcomes,
particularly
resource-limited
settings.
Language: Английский
Artificial intelligence and machine learning in critical care research
Journal of Critical Care,
Journal Year:
2024,
Volume and Issue:
82, P. 154791 - 154791
Published: March 25, 2024
Language: Английский
Optimizing Lung Condition Categorization through a Deep Learning Approach to Chest X-ray Image Analysis
Theodora Sanida,
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Maria Vasiliki Sanida,
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Argyrios Sideris
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et al.
BioMedInformatics,
Journal Year:
2024,
Volume and Issue:
4(3), P. 2002 - 2021
Published: Sept. 10, 2024
Background:
Evaluating
chest
X-rays
is
a
complex
and
high-demand
task
due
to
the
intrinsic
challenges
associated
with
diagnosing
wide
range
of
pulmonary
conditions.
Therefore,
advanced
methodologies
are
required
categorize
multiple
conditions
from
X-ray
images
accurately.
Methods:
This
study
introduces
an
optimized
deep
learning
approach
designed
for
multi-label
categorization
images,
covering
broad
spectrum
conditions,
including
lung
opacity,
normative
states,
COVID-19,
bacterial
pneumonia,
viral
tuberculosis.
An
model
based
on
modified
VGG16
architecture
SE
blocks
was
developed
applied
large
dataset
images.
The
evaluated
against
state-of-the-art
techniques
using
metrics
such
as
accuracy,
F1-score,
precision,
recall,
area
under
curve
(AUC).
Results:
VGG16-SE
demonstrated
superior
performance
across
all
metrics.
achieved
accuracy
98.49%,
F1-score
98.23%,
precision
98.41%,
recall
98.07%
AUC
98.86%.
Conclusion:
provides
effective
categorizing
X-rays.
model’s
high
various
suggests
its
potential
integration
into
clinical
workflows,
enhancing
speed
disease
diagnosis.
Language: Английский
Lung X-ray image segmentation algorithm based on Multihead Self-Attention Mechanism (MSAG) optimizing Unet networks
Applied and Computational Engineering,
Journal Year:
2024,
Volume and Issue:
67(1), P. 160 - 166
Published: July 16, 2024
In
this
paper,
the
Multihead
Self-Attention
Mechanism
(MSAG)
is
used
to
optimize
Unet
network
for
accurate
segmentation
of
lung
X-ray
images.
By
introducing
MSAG
module,
ability
capture
global
and
local
correlations
enhanced,
which
effectively
improves
accuracy
results.
The
introduction
multi-head
self-attention
mechanism
enables
have
more
powerful
modelling
generalization
capabilities,
can
process
various
types
images
stably
efficiently.
dataset
divided
into
training,
validation
test
sets
according
ratio
4:3:3.
loss
gradually
converges
during
training
process,
model
learns
data
features
patterns,
gap
between
them
real
labels
reduced.
performance
on
set
good
no
over-fitting
occurs,
demonstrating
generalize
unseen
data.
evaluation
metrics
show
an
IoU
0.85,
a
Dice
0.92,
Accuracy
0.88,
proving
that
accurately
extract
segmentation.
This
study
has
achieved
satisfactory
results
in
field
medical
by
optimizing
structure
new
techniques,
are
positive
significance
improving
efficiency
image
Language: Английский
Modeling and prediction of set‑up errors in breast cancer image‑guided radiotherapy using the Gaussian mixture model
Fangfen Dong,
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Jing Chen,
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Liu Feiyu
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et al.
Oncology Letters,
Journal Year:
2024,
Volume and Issue:
28(6)
Published: Sept. 30, 2024
The
aim
of
the
present
study
was
to
develop
a
prediction
model
for
set-up
error
distribution
in
breast
cancer
image-guided
radiotherapy
(IGRT)
using
Gaussian
mixture
(GMM).
To
achieve
this,
errors
data
80
patients
with
were
selected,
and
GMM
used
model.
predicted
center
points,
covariance
probability
calculated
compared
planning
target
volume
(PTV)
margin
formula.
A
total
1,200
sets
IGRT
collected.
results
parameters
showed
that
mainly
direction
µ
Language: Английский