2022 9th International Conference on Computing for Sustainable Global Development (INDIACom),
Год журнала:
2024,
Номер
unknown, С. 741 - 744
Опубликована: Фев. 28, 2024
In
this
study,
we
introduce
web-based
inventory,
stock
monitoring,
and
control
systems
powered
by
a
local
encrypted
web
server.
Through
locally
secured
servers,
novel
technology
emphasizes
data
protection,
accessibility,
while
streamlining
the
installation
of
conventional
software
providing
robust
platform
accessible
from
any
device
with
regular
browser.
Real-time
inventory
control,
user
authentication,
protected
servers
for
increased
privacy
are
some
system's
key
features.
With
aid
technology,
businesses
can
keep
an
eye
on
their
in
real
time,
enabling
proactive
decision-making
lowering
likelihood
inventory-related
problems.
user-friendly
online
interface,
users
easily
manage
adding,
updating,
or
deleting
goods,
improving
accuracy
reducing
mistakes.
The
capacity
to
customize
system
meet
needs
individual
firms,
whether
small
large,
is
distinguishing
feature.
To
accommodate
more
users,
locations
as
company
change,
grows
effortlessly.
An
automated
computing
solution
based
electronic
recording
processing
report
production
was
developed
part
overcome
these
difficulties.
Bioengineering,
Год журнала:
2024,
Номер
11(12), С. 1302 - 1302
Опубликована: Дек. 23, 2024
Accurate
segmentation
of
brain
tumors
in
MRI
scans
is
critical
for
diagnosis
and
treatment
planning.
Traditional
models,
such
as
U-Net,
excel
capturing
spatial
information
but
often
struggle
with
complex
tumor
boundaries
subtle
variations
image
contrast.
These
limitations
can
lead
to
inconsistencies
identifying
regions,
impacting
the
accuracy
clinical
outcomes.
To
address
these
challenges,
this
paper
proposes
a
novel
modification
U-Net
architecture
by
integrating
attention
mechanism
designed
dynamically
focus
on
relevant
regions
within
scans.
This
innovation
enhances
model's
ability
delineate
fine
improves
precision.
Our
model
was
evaluated
Figshare
dataset,
which
includes
annotated
images
meningioma,
glioma,
pituitary
tumors.
The
proposed
achieved
Dice
similarity
coefficient
(DSC)
0.93,
recall
0.95,
an
AUC
0.94,
outperforming
existing
approaches
V-Net,
DeepLab
V3+,
nnU-Net.
results
demonstrate
effectiveness
our
addressing
key
challenges
like
low-contrast
boundaries,
small
overlapping
Furthermore,
lightweight
design
ensures
its
suitability
real-time
applications,
making
it
robust
tool
automated
segmentation.
study
underscores
potential
mechanisms
significantly
enhance
medical
imaging
models
paves
way
more
effective
diagnostic
tools.
Sensors,
Год журнала:
2025,
Номер
25(9), С. 2746 - 2746
Опубликована: Апрель 26, 2025
A
brain
tumor
is
the
result
of
abnormal
growth
cells
in
central
nervous
system
(CNS),
widely
considered
as
a
complex
and
diverse
clinical
entity
that
difficult
to
diagnose
cure.
In
this
study,
we
focus
on
current
advances
medical
imaging,
particularly
magnetic
resonance
imaging
(MRI),
how
machine
learning
(ML)
deep
(DL)
algorithms
might
be
combined
with
assessments
improve
diagnosis.
Due
its
superior
contrast
resolution
safety
compared
other
methods,
MRI
highlighted
preferred
modality
for
tumors.
The
challenges
related
analysis
different
processes
including
detection,
segmentation,
classification,
survival
prediction
are
addressed
along
ML/DL
approaches
significantly
these
steps.
We
systematically
analyzed
107
studies
(2018–2024)
employing
ML,
DL,
hybrid
models
across
publicly
available
datasets
such
BraTS,
TCIA,
Figshare.
light
recent
developments
analysis,
many
have
been
proposed
accurately
obtain
ontological
characteristics
tumors,
enhancing
diagnostic
precision
personalized
therapeutic
strategies.
