Microscopy Research and Technique,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 4, 2025
ABSTRACT
This
article
proposes
a
method
called
DenseNet
121‐Mask
R‐CNN
(DN‐MRCNN)
for
the
detection
and
segmentation
of
brain
tumors.
The
main
objective
is
to
reduce
execution
time
accurately
locate
segment
tumor,
including
its
subareas.
input
images
undergo
preprocessing
techniques
such
as
median
filtering
Gaussian
noise
artifacts,
well
improve
image
quality.
Histogram
equalization
used
enhance
tumor
regions,
augmentation
employed
model's
diversity
robustness.
To
capture
important
patterns,
gated
axial
self‐attention
layer
added
121
model,
allowing
increased
attention
during
analysis
images.
For
accurate
segmentation,
boundary
boxes
are
generated
using
Regional
Proposal
Network
with
anchor
customization.
Post‐processing
techniques,
specifically
nonmaximum
suppression,
performed
neglect
redundant
bounding
caused
by
overlapping
regions.
Mask
model
detect
entire
(WT),
core
(TC),
enhancing
(ET).
proposed
evaluated
BraTS
2019
dataset,
UCSF‐PDGM
UPENN‐GBM
which
commonly
segmentation.
IEEE Access,
Год журнала:
2024,
Номер
12, С. 26875 - 26896
Опубликована: Янв. 1, 2024
The
human
brain
is
an
incredible
and
wonderful
organ
that
governs
all
body
actions.
Due
to
its
great
importance,
any
defect
in
the
shape
of
regions
should
be
reported
quickly
reduce
death
rate.
abnormal
region
segmentation
helps
plan
monitor
treatment.
most
critical
procedure
isolating
normal
tissues
from
each
other.
So
far,
remarkable
imaging
modalities
are
being
used
diagnose
abnormalities
at
their
early
stages,
magnetic
resonance
(MRI)
renowned
noninvasive
among
those
modalities.
This
paper
investigates
current
landscape
tumor
(BTS)
by
exploring
emerging
deep
learning
(DL)
methods
for
MRI
analysis.
findings
offer
a
comprehensive
comparison
recent
DL
approaches,
emphasizing
effectiveness
handling
diverse
types
while
addressing
limitations
associated
with
data
scarcity
robust
validation.
has
shown
vital
improvement
BTS,
so
our
primary
focus
include
significant
models
analyze
MRI.
However,
outperforms
traditional
methods;
still,
there
several
limitations,
especially
related
types,
lack
datasets,
weak
validations.
future
perspectives
DL-based
BTS
present
potential
revolutionizing
diagnosis
treatment
tumors.
ACM Transactions on Multimedia Computing Communications and Applications,
Год журнала:
2024,
Номер
20(7), С. 1 - 19
Опубликована: Март 23, 2024
Accurate
and
automated
segmentation
of
lesions
in
brain
MRI
scans
is
crucial
diagnostics
treatment
planning.
Despite
the
significant
achievements
existing
approaches,
they
often
require
substantial
computational
resources
fail
to
fully
exploit
synergy
between
low-level
high-level
features.
To
address
these
challenges,
we
introduce
Separable
Spatial
Convolutional
Network
(SSCN),
an
innovative
model
that
refines
U-Net
architecture
achieve
efficient
tumor
with
minimal
cost.
SSCN
integrates
PocketNet
paradigm
replaces
standard
convolutions
depthwise
separable
convolutions,
resulting
a
reduction
parameters
load.
Additionally,
our
feature
complementary
module
enhances
interaction
features
across
encoder-decoder
structure,
facilitating
integration
multi-scale
while
maintaining
low
demands.
The
also
incorporates
spatial
attention
mechanism,
enhancing
its
capability
discern
details.
Empirical
validations
on
datasets
demonstrate
effectiveness
proposed
model,
especially
segmenting
small
medium-sized
tumors,
only
0.27M
3.68
GFlops.
Our
code
available
at
https://github.com/zzpr/SSCN
.
Diagnostics,
Год журнала:
2025,
Номер
15(2), С. 168 - 168
Опубликована: Янв. 13, 2025
Background:
Artificial
intelligence
(AI)
has
recently
made
unprecedented
contributions
in
every
walk
of
life,
but
it
not
been
able
to
work
its
way
into
diagnostic
medicine
and
standard
clinical
practice
yet.
Although
data
scientists,
researchers,
medical
experts
have
working
the
direction
designing
developing
computer
aided
diagnosis
(CAD)
tools
serve
as
assistants
doctors,
their
large-scale
adoption
integration
healthcare
system
still
seems
far-fetched.
Diagnostic
radiology
is
no
exception.
Imagining
techniques
like
magnetic
resonance
imaging
(MRI),
computed
tomography
(CT),
positron
emission
(PET)
scans
widely
very
effectively
employed
by
radiologists
neurologists
for
differential
diagnoses
neurological
disorders
decades,
yet
AI-powered
systems
analyze
such
incorporated
operating
procedures
systems.
Why?
It
absolutely
understandable
that
medicine,
precious
human
lives
are
on
line,
hence
there
room
even
tiniest
mistakes.
Nevertheless,
with
advent
explainable
artificial
(XAI),
old-school
black
boxes
deep
learning
(DL)
unraveled.
Would
XAI
be
turning
point
finally
embrace
AI
radiology?
This
review
a
humble
endeavor
find
answers
these
questions.
Methods:
In
this
review,
we
present
journey
recognize,
preprocess,
brain
MRI
various
disorders,
special
emphasis
CAD
embedded
explainability.
A
comprehensive
literature
from
2017
2024
was
conducted
using
host
databases.
We
also
domain
experts’
opinions
summarize
challenges
up
ahead
need
addressed
order
fully
exploit
tremendous
potential
application
diagnostics
humanity.
Results:
Forty-seven
studies
were
summarized
tabulated
information
about
technology
datasets
employed,
along
performance
accuracies.
The
strengths
weaknesses
discussed.
addition,
seven
around
world
presented
guide
engineers
scientists
tools.
Conclusions:
Current
research
observed
focused
enhancement
accuracies
DL
regimens,
less
attention
being
paid
authenticity
usefulness
explanations.
shortage
ground
truth
explainability
observed.
Visual
explanation
methods
found
dominate;
however,
they
might
enough,
more
thorough
professor-like
explanations
would
required
build
trust
professionals.
Special
factors
legal,
ethical,
safety,
security
issues
can
bridge
current
gap
between
routine
practice.
Microscopy Research and Technique,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 4, 2025
ABSTRACT
This
article
proposes
a
method
called
DenseNet
121‐Mask
R‐CNN
(DN‐MRCNN)
for
the
detection
and
segmentation
of
brain
tumors.
The
main
objective
is
to
reduce
execution
time
accurately
locate
segment
tumor,
including
its
subareas.
input
images
undergo
preprocessing
techniques
such
as
median
filtering
Gaussian
noise
artifacts,
well
improve
image
quality.
Histogram
equalization
used
enhance
tumor
regions,
augmentation
employed
model's
diversity
robustness.
To
capture
important
patterns,
gated
axial
self‐attention
layer
added
121
model,
allowing
increased
attention
during
analysis
images.
For
accurate
segmentation,
boundary
boxes
are
generated
using
Regional
Proposal
Network
with
anchor
customization.
Post‐processing
techniques,
specifically
nonmaximum
suppression,
performed
neglect
redundant
bounding
caused
by
overlapping
regions.
Mask
model
detect
entire
(WT),
core
(TC),
enhancing
(ET).
proposed
evaluated
BraTS
2019
dataset,
UCSF‐PDGM
UPENN‐GBM
which
commonly
segmentation.
