Integrating Deep Learning and MRQy: A Comprehensive Framework for Early Detection and Quality Control of Brain Tumors in MRI Images using Python
International Journal of Computational and Experimental Science and Engineering,
Год журнала:
2025,
Номер
11(2)
Опубликована: Апрель 15, 2025
The
early
detection
of
brain
tumors
is
crucial
for
timely
medical
intervention
and
improved
patient
survival
rates.
Magnetic
Resonance
Imaging
(MRI)
the
gold
standard
tumor
diagnosis
due
to
its
superior
soft-tissue
contrast
non-invasive
nature.
However,
variations
in
MRI
quality,
including
noise,
artifacts,
scanner
inconsistencies,
can
impact
diagnostic
accuracy.
This
study
aims
de-velop
a
Python-based
deep-learning
model
scans
while
integrating
an
automated
quality
control
system
using
MRQy.
MRQy,
open-source
tool,
facilitates
assessment
by
evaluating
signal-to-noise
ratios
(SNR),
contrast-to-noise
(CNR),
motion-related
artifacts.
deep
learning
will
be
trained
on
meticulously
curated
dataset,
ensur-ing
high-quality
artifact-free
images.
By
combining
MRQy’s
capabilities
with
techniques,
expected
en-hance
accuracy
reduce
false-positive
false-negative
Furthermore,
this
research
underscores
significance
standardized
imaging
protocols
minimize
variability
across
scanners
institutions,
ensuring
repro-ducibility
clinical
AI
applications.
proposed
approach
leverages
modern
convolutional
neural
networks
(CNNs)
transfer
incorpo-rating
pre-trained
architectures
such
as
Res
Net
Efficient
enhance
fea-ture
extraction.
MRQy-based
AI-driven
classification,
optimize
MRI-based
diagnostics,
human
error,
improve
outcomes.
findings
contribute
ad-vancement
AI-powered
highlight
importance
Язык: Английский
Enhanced framework for credit card fraud detection using robust feature selection and a stacking ensemble model approach
Results in Engineering,
Год журнала:
2025,
Номер
unknown, С. 105084 - 105084
Опубликована: Апрель 1, 2025
Язык: Английский
Application of Remote Sensing and GIS in Monitoring Forest Cover Changes in Vietnam Based on Natural Zoning
Land,
Год журнала:
2025,
Номер
14(5), С. 1037 - 1037
Опубликована: Май 9, 2025
Forest
cover
changes
monitoring
in
Vietnam
has
been
conducted
using
remote
sensing
(RS)
and
geographic
information
systems
(GIS).
Given
Vietnam’s
diverse
climate,
this
study
focused
on
the
Thanh
Hoa,
Kon
Tum,
Dong
Nai
provinces
due
to
their
distinct
natural
conditions
forest
structures.
Land
was
classified
into
five
categories:
broadleaf
forests,
mixed
shrubland/grassland/agricultural
land,
non-forested
areas,
water
bodies.
RS
data
processing
performed
Google
Earth
Engine
(GEE),
with
land
classification
via
Random
algorithm.
The
findings
revealed
significant
between
2010
2020.
In
forests
expanded
by
51.15%
(91,159
ha),
while
declined
19.68%
(105,445
ha).
Tum
experienced
reductions
both
(20.05%,
26,685
ha)
(4.06%,
20,501
Meanwhile,
recorded
increases
(29.15%,
23,263
(12.17%,
20,632
study’s
reliability
confirmed
a
Kappa
coefficient
of
0.81–0.89.
To
predict
changes,
two
methods—the
CA-Markov
model
MOLUSCE
module—were
compared.
Results
demonstrated
that
module
achieved
higher
accuracy,
deviations
from
actual
1.61,
1.14,
1.80
for
Nai,
respectively,
whereas
yielded
larger
(8.79,
6.29,
5.03).
Future
projections
2030,
generated
MOLUSCE,
suggest
impacts
agricultural
expansion,
deforestation,
restoration
efforts
area.
This
highlights
advantages
GIS
complex
sustainable
management
Vietnam.
Язык: Английский