BioMedical Engineering OnLine,
Journal Year:
2024,
Volume and Issue:
23(1)
Published: Dec. 23, 2024
This
study
aims
to
accurately
predict
the
effects
of
hormonal
therapy
on
prostate
cancer
(PC)
lesions
by
integrating
multi-modality
magnetic
resonance
imaging
(MRI)
and
clinical
marker
prostate-specific
antigen
(PSA).
It
addresses
limitations
Convolutional
Neural
Networks
(CNNs)
in
capturing
long-range
spatial
relations
Vision
Transformer
(ViT)'s
deficiency
localization
information
due
consecutive
downsampling.
The
research
question
focuses
improving
PC
response
prediction
accuracy
combining
both
approaches.
We
propose
a
3D
multi-branch
CNN
(CNNFormer)
model,
ViT.
Each
branch
model
utilizes
encode
volumetric
images
into
high-level
feature
representations,
preserving
detailed
localization,
while
ViT
extracts
global
salient
features.
framework
was
evaluated
39-individual
patient
cohort,
stratified
PSA
biomarker
status.
Our
achieved
remarkable
performance
differentiating
responders
non-responders
therapy,
with
an
97.50%,
sensitivity
100%,
specificity
95.83%.
These
results
demonstrate
effectiveness
CNNFormer
despite
cohort's
small
size.
findings
emphasize
framework's
potential
enhancing
personalized
treatment
planning
monitoring.
By
strengths
ViT,
proposed
approach
offers
robust,
accurate
implications
for
decision-making.
Intelligent Systems with Applications,
Journal Year:
2024,
Volume and Issue:
23, P. 200399 - 200399
Published: June 20, 2024
The
accurate
classification
of
endoscopic
images
is
a
challenging
yet
critical
task
in
medical
diagnostics,
which
directly
affects
the
treatment
and
management
Gastrointestinal
diseases.
Misclassification
can
lead
to
incorrect
plans,
adversely
affecting
patient
outcomes.
To
address
this
challenge,
our
research
aimed
develop
reliable
computational
model
improve
accuracy
classifying
conditions
esophagitis
polyps.
We
focused
on
subset
Kvasir
v1
secondary
dataset,
comprising
2000
evenly
distributed
across
two
classes:
polyp.
goal
was
leverage
strengths
both
Machine
Learning(ML)
Deep
Learning(DL)
create
that
not
only
predicts
with
high
but
also
integrates
seamlessly
into
clinical
workflows.
end,
we
introduced
novel
VRG-based
ensemble
image
feature
extraction
technique,
combining
powers
VGG,
RF,
GB
models
synthesize
robust
set
conducive
high-precision
classification.
approach
demonstrated
best-in-class
performance
achieving
an
outstanding
99.73%
detecting
practical
implications
these
results
are
substantial,
indicating
method
significantly
diagnostic
real-world
settings,
reduce
rate
misdiagnosis,
contribute
efficient
effective
patients,
ultimately
enhancing
quality
healthcare
services.
With
successful
application
proposed
controlled
future
work
involves
deploying
environments
expanding
its
broader
spectrum
multi-class
datasets.
Neural Computing and Applications,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 27, 2024
Abstract
One
of
the
most
common
cancers
among
women
worldwide
is
breast
cancer
(BC),
and
early
diagnosis
can
save
lives.
Early
detection
BC
increases
likelihood
a
successful
outcome
by
enabling
treatment
to
start
sooner.
Even
in
areas
without
access
specialist
physician,
machine
learning
(ML)
aids
detection.
The
medical
imaging
community
becoming
more
interested
using
ML,
deep
(DL)
increase
accuracy
screening.
Many
disease-related
data
are
sparse.
However,
for
DL
models
perform
well,
large
amount
required.
Because
this,
that
currently
use
on
images
not
as
effective
they
could
be.
Convolutional
neural
network
(CNN)
have
recently
gained
popularity
industry,
admirably
terms
high
performance
robustness
at
image
classification.
proposed
method
classifies
ensemble
pre-trained
such
dense
convolutional
(DenseNet)-121
EfficientNet-B5
feature
extractor
networks,
well
support
vector
Using
modified
meta-heuristic
optimizer,
selected
CNN
hyperparameters
were
optimized
improve
performance.
experimental
results
presented
model
INbreast
dataset
show
classification,
with
overall
accuracy,
sensitivity,
specificity,
precision,
area
under
ROC
curve
(AUC)
values
99.9%,
99.8%,
99.1%,
1.0,
respectively.
Diagnostics,
Journal Year:
2024,
Volume and Issue:
14(22), P. 2576 - 2576
Published: Nov. 15, 2024
Prostate
cancer
is
a
leading
cause
of
cancer-related
deaths
in
men
worldwide,
making
accurate
diagnosis
critical
for
effective
treatment.
Recent
advancements
artificial
intelligence
(AI)
and
machine
learning
(ML)
have
shown
promise
improving
the
diagnostic
accuracy
prostate
cancer.
