International Journal of Electronics and Communication Engineering,
Год журнала:
2024,
Номер
11(11), С. 228 - 243
Опубликована: Ноя. 30, 2024
Cervical
cancer,
a
malignant
tumour
that
forms
in
the
cervix,
significantly
contributes
to
cancer-related
mortality
among
women
globally,
making
early
diagnosis
crucial
for
effective
treatment.
Pap
smear
images,
which
are
microscopic
images
of
cervical
cells,
commonly
used
detection
abnormal
cells
may
lead
cancer.
This
study
introduces
novel
classification
approach,
Variable
Kernel
Feature
Fusion-CNN
(VKFF-CNN),
improves
performance
by
fusing
multi-scale
features
using
convolutional
layers
with
3x3,
4x4,
and
5x5
kernels.
architecture
captures
diverse
set
features,
enhancing
ability
model
accurately
classify
cells.
With
an
average
accuracy
98.03%,
precision
97.83%,
recall
97.11%,
F1
score
98.23%,
VKFF-CNN
exhibited
outstanding
outcomes
on
Herlev
Smear
dataset.
These
results
demonstrate
outperforms
traditional
machine
learning
models.
The
model's
confusion
matrix
indicated
fewer
misclassifications,
underscoring
its
robustness
effectiveness.
Including
batch
normalization
softmax
activation
function
further
enhanced
stability
accurate
classification.
Overall,
presents
promising
advancement
automated
cancer
screening,
providing
highly
reliable
detection.
Current Opinion in Ophthalmology,
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 29, 2024
Purpose
of
review
The
purpose
this
was
to
provide
a
summary
currently
available
retinal
imaging
and
visual
function
testing
methods
for
assessing
inherited
degenerations
(IRDs),
with
the
emphasis
on
application
deep
learning
(DL)
approaches
assist
determination
structural
biomarkers
IRDs.
Recent
findings
(clinical
trials
IRDs;
discover
effective
as
endpoints;
DL
applications
in
processing
images
detect
disease-related
changes)
Summary
Assessing
photoreceptor
loss
is
direct
way
evaluate
Outer
layer
structures,
including
outer
nuclear
layer,
ellipsoid
zone,
segment,
RPE,
are
potential
More
work
may
be
needed
structure
relationship.
International Journal of Electronics and Communication Engineering,
Год журнала:
2024,
Номер
11(11), С. 228 - 243
Опубликована: Ноя. 30, 2024
Cervical
cancer,
a
malignant
tumour
that
forms
in
the
cervix,
significantly
contributes
to
cancer-related
mortality
among
women
globally,
making
early
diagnosis
crucial
for
effective
treatment.
Pap
smear
images,
which
are
microscopic
images
of
cervical
cells,
commonly
used
detection
abnormal
cells
may
lead
cancer.
This
study
introduces
novel
classification
approach,
Variable
Kernel
Feature
Fusion-CNN
(VKFF-CNN),
improves
performance
by
fusing
multi-scale
features
using
convolutional
layers
with
3x3,
4x4,
and
5x5
kernels.
architecture
captures
diverse
set
features,
enhancing
ability
model
accurately
classify
cells.
With
an
average
accuracy
98.03%,
precision
97.83%,
recall
97.11%,
F1
score
98.23%,
VKFF-CNN
exhibited
outstanding
outcomes
on
Herlev
Smear
dataset.
These
results
demonstrate
outperforms
traditional
machine
learning
models.
The
model's
confusion
matrix
indicated
fewer
misclassifications,
underscoring
its
robustness
effectiveness.
Including
batch
normalization
softmax
activation
function
further
enhanced
stability
accurate
classification.
Overall,
presents
promising
advancement
automated
cancer
screening,
providing
highly
reliable
detection.
