Frontiers in Applied Mathematics and Statistics,
Год журнала:
2023,
Номер
9
Опубликована: Дек. 6, 2023
Radiologists
confront
formidable
challenges
when
confronted
with
the
intricate
task
of
classifying
brain
tumors
through
analysis
MRI
images.
Our
forthcoming
manuscript
introduces
an
innovative
and
highly
effective
methodology
that
capitalizes
on
capabilities
Least
Squares
Support
Vector
Machines
(LS-SVM)
in
tandem
rich
insights
drawn
from
Multi-Scale
Morphological
Texture
Features
(MMTF)
extracted
T1-weighted
MR
underwent
meticulous
evaluation
a
substantial
dataset
encompassing
139
cases,
consisting
119
cases
aberrant
20
normal
The
outcomes
we
achieved
are
nothing
short
extraordinary.
LS-SVM-based
approach
vastly
outperforms
competing
classifiers,
demonstrating
its
dominance
exceptional
accuracy
rate
98.97%.
This
represents
3.97%
improvement
over
alternative
methods,
accompanied
by
notable
2.48%
enhancement
Sensitivity
10%
increase
Specificity.
These
results
conclusively
surpass
performance
traditional
classifiers
such
as
(SVM),
Radial
Basis
Function
(RBF),
Artificial
Neural
Networks
(ANN)
terms
classification
accuracy.
outstanding
our
model
realm
tumor
diagnosis
signifies
leap
forward
field,
holding
promise
delivering
more
precise
dependable
tools
for
radiologists
healthcare
professionals
their
pivotal
role
identifying
using
imaging
techniques.
International Journal of Intelligent Systems,
Год журнала:
2021,
Номер
37(8), С. 4967 - 4993
Опубликована: Ноя. 19, 2021
Ethiopia's
coffee
export
accounts
for
about
34%
of
all
exports
the
budget
year
2019/2020.
Making
it
10th-largest
exporter
in
world.
Coffee
diseases
cause
around
30%
loss
production
annually.
In
this
paper,
we
propose
an
approach
detection
four
classes
leaf
diseases,
Rust,
Miner,
Cercospora,
and
Phoma
by
using
a
fast
Hue,
Saturation,
Value
(HSV)
color
space
segmentation
MobileNetV2
architecture
trained
transfer
learning.
The
proposed
HSV
algorithm
constitutes
separating
from
background
infected
spots
on
automatically
finding
best
threshold
value
Saturation
(S)
channel
space.
was
compared
to
YCgCr
k-means
algorithms,
terms
Mean
Intersection
Over
Union
F1-Score.
outperformed
these
methods
achieved
MIoU
score
72.13%
F1
82.54%.
also
outperforms
execution
time,
taking
average
0.02
s
per
image
diseased
healthy
spots.
Our
classifier
96%
classification
accuracy
precision.
faster
make
suitable
deployment
mobile
devices
as
such
has
been
successfully
implemented
smartphones
running
Android
operating
system.
Biomedicines,
Год журнала:
2022,
Номер
10(1), С. 96 - 96
Опубликована: Янв. 3, 2022
The
current
treatment
for
malignant
brain
tumors
includes
surgical
resection,
radiotherapy,
and
chemotherapy.
Nevertheless,
the
survival
rate
patients
with
glioblastoma
multiforme
(GBM)
a
high
grade
of
malignancy
is
less
than
one
year.
From
clinical
point
view,
effective
GBM
limited
by
several
challenges.
First,
anatomical
complexity
influences
extent
resection
because
fine
balance
must
be
struck
between
maximal
removal
tissue
minimal
risk.
Second,
central
nervous
system
has
distinct
microenvironment
that
protected
blood-brain
barrier,
restricting
systemically
delivered
drugs
from
accessing
brain.
Additionally,
characterized
intra-tumor
inter-tumor
heterogeneity
at
cellular
histological
levels.
This
peculiarity
GBM-constituent
tissues
induces
different
responses
to
therapeutic
agents,
leading
failure
targeted
therapies.
Unlike
photodynamic
therapy
(PDT)
can
treat
micro-invasive
areas
while
protecting
sensitive
regions.
PDT
involves
photoactivation
photosensitizers
(PSs)
are
selectively
incorporated
into
tumor
cells.
Photo-irradiation
activates
PS
transfer
energy,
resulting
in
production
reactive
oxygen
species
induce
cell
death.
Clinical
outcomes
PDT-treated
advanced
terms
nanomedicine.
review
discusses
applications
nanomedicine
GBM.
medRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2023,
Номер
unknown
Опубликована: Июнь 30, 2023
ABSTRACT
Purpose
Artificial
intelligence
(AI)-automated
tumor
delineation
for
pediatric
gliomas
would
enable
real-time
volumetric
evaluation
to
support
diagnosis,
treatment
response
assessment,
and
clinical
decision-making.
Auto-segmentation
algorithms
tumors
are
rare,
due
limited
data
availability,
have
yet
demonstrate
translation.
Methods
We
leveraged
two
datasets
from
a
national
brain
consortium
(n=184)
cancer
center
(n=100)
develop,
externally
validate,
clinically
benchmark
deep
learning
neural
networks
low-grade
glioma
(pLGG)
segmentation
using
novel
in-domain,
stepwise
transfer
approach.
The
best
model
[via
Dice
similarity
coefficient
(DSC)]
was
validated
subject
randomized,
blinded
by
three
expert
clinicians
wherein
assessed
acceptability
of
expert-
AI-generated
segmentations
via
10-point
Likert
scales
Turing
tests.
Results
AI
utilized
(median
DSC:
0.877
[IQR
0.715-0.914])
versus
baseline
DSC
0.812
0.559-0.888];
p
<0.05).
On
external
testing
(n=60),
the
yielded
accuracy
comparable
inter-expert
agreement
0.834
0.726-0.901]
vs.
0.861
0.795-0.905],
=0.13).
benchmarking
(n=100
scans,
300
3
experts),
experts
rated
higher
on
average
compared
other
rating:
9
7-9])
7
7-9],
<0.05
each).
Additionally,
had
significantly
(
<0.05)
overall
(80.2%
65.4%).
Experts
correctly
predicted
origins
in
an
26.0%
cases.
Conclusions
Stepwise
enabled
expert-level,
automated
auto-segmentation
measurement
with
high
level
acceptability.
This
approach
may
development
translation
imaging
scenarios.
Summary
Authors
proposed
develop
validate
whose
performance
were
par
neuroradiologists
radiation
oncologists.
Key
Points
There
available
train
tumors,
adult-centric
models
generalize
poorly
setting.
demonstrated
gains
(Dice
score:
methodologies
human
validation.
testing,
received
score
rating
Transfer-Encoder
expert:
80.2%
65.4%)
tests
showed
uniformly
low
ability
experts’
identify
as
human-generated
(mean
accuracy:
26%).
Frontiers in Applied Mathematics and Statistics,
Год журнала:
2023,
Номер
9
Опубликована: Дек. 6, 2023
Radiologists
confront
formidable
challenges
when
confronted
with
the
intricate
task
of
classifying
brain
tumors
through
analysis
MRI
images.
Our
forthcoming
manuscript
introduces
an
innovative
and
highly
effective
methodology
that
capitalizes
on
capabilities
Least
Squares
Support
Vector
Machines
(LS-SVM)
in
tandem
rich
insights
drawn
from
Multi-Scale
Morphological
Texture
Features
(MMTF)
extracted
T1-weighted
MR
underwent
meticulous
evaluation
a
substantial
dataset
encompassing
139
cases,
consisting
119
cases
aberrant
20
normal
The
outcomes
we
achieved
are
nothing
short
extraordinary.
LS-SVM-based
approach
vastly
outperforms
competing
classifiers,
demonstrating
its
dominance
exceptional
accuracy
rate
98.97%.
This
represents
3.97%
improvement
over
alternative
methods,
accompanied
by
notable
2.48%
enhancement
Sensitivity
10%
increase
Specificity.
These
results
conclusively
surpass
performance
traditional
classifiers
such
as
(SVM),
Radial
Basis
Function
(RBF),
Artificial
Neural
Networks
(ANN)
terms
classification
accuracy.
outstanding
our
model
realm
tumor
diagnosis
signifies
leap
forward
field,
holding
promise
delivering
more
precise
dependable
tools
for
radiologists
healthcare
professionals
their
pivotal
role
identifying
using
imaging
techniques.