The
most
prevalent
primary
brain
tumor,
glioma,
is
caused
by
glial
cell
carcinogenesis
in
the
central
nervous
system.
For
numerous
applications
area
of
health
care
evaluation,
tumor
localization
and
separation
from
magnetic
resonance
images
(MRI)
are
challenging
yet
crucial
tasks.
Several
recently
developed
methods
utilized
four
modalities:
T1,
T1c,
T2,
FLAIR.
This
because
each
imaging
modality
provides
distinct
important
information
concerning
every
region
tumor.
process
diagnosis,
therapy
selection,
risk
variables
detection
depends
on
trustworthy
precise
segmentation
survival
patients
forecasting.
In
this
article,
a
state-of-the-art
fuzzy-based
system
introduced
that
uses
multimodal
MRI
to
categorize
tumors
estimate
glioma
survival.
To
address
drawbacks
FCM,
suggested
approach
combined
weight
function
with
conventional
Fuzzy
C-means
(FCM).
Extensive
tests
carried
out
different
BRATS
challenge
datasets,
demonstrating
achieves
competitive
outcomes.
Evaluation
BraTs
dataset
confirms
effectiveness
Weighted
FCM
(WFCM),
segmented
results
compared
ground
truth
images.
A
small
number
performance
metrics
were
also
used
for
assessing
qualitative
as
well
quantitative
resulting
dissected
help
medical
professionals
diagnose,
medicate,
or
plan
intervention
affected
individuals
earlier.
Engineering Technology & Applied Science Research,
Journal Year:
2024,
Volume and Issue:
14(2), P. 13695 - 13701
Published: April 2, 2024
With
the
exponential
growth
of
medical
data,
Machine
Learning
(ML)
algorithms
are
becoming
increasingly
important
to
management
and
organization
healthcare
information.
This
study
aims
explore
role
that
ML
can
play
in
optimizing
records,
by
identifying
challenges,
advantages,
limitations
associated
with
this
technology.
Consequently,
current
will
contribute
understanding
how
might
be
applied
industry
a
variety
circumstances.
Using
findings
study,
professionals,
researchers,
policymakers
able
make
informed
decisions
regarding
adoption
implementation
techniques
for
regulating
records.
The
paper
revealed
an
efficiently
directing
classifying
records
using
different
perspectives.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Feb. 25, 2025
The
secure
storage
and
transmission
of
healthcare
data
have
become
a
critical
concern
due
to
their
increasing
use
in
the
diagnosis
treatment
various
diseases.
Medical
images
contain
confidential
patient
information,
unauthorized
access
or
modification
these
can
severe
consequences.
Chaotic
maps
are
commonly
used
for
constructing
medical
image
cipher
systems,
but
with
growth
quantum
technology,
systems
may
vulnerable.
To
address
this
issue,
new
algorithm
based
on
cascading
walk
Chebyshev
map
has
been
presented
paper.
proposed
system
tested
found
high
levels
security
efficiency,
UACI,
NPCR,
Chi-square,
global
information
entropy
values
averaging
at
33.48095%,
99.62984%,
248.92128,
7.99923,
respectively.
Technology in Cancer Research & Treatment,
Journal Year:
2011,
Volume and Issue:
10(5), P. 443 - 455
Published: Oct. 1, 2011
In
the
field
of
quantitative
microscopy,
textural
information
plays
a
significant
role
very
often
in
tissue
characterization
and
diagnosis,
addition
to
morphology
intensity.
The
objective
this
work
is
improve
classification
accuracy
based
on
features
for
development
computer
assisted
screening
oral
sub-mucous
fibrosis
(OSF).
fact,
approach
introduced
used
grade
histopathological
sections
into
normal,
OSF
without
dysplasia
(OSFWD)
with
(OSFD),
which
would
help
onco-pathologists
screen
subjects
rapidly.
main
evaluate
use
Higher
Order
Spectra
(HOS)
Local
Binary
Pattern
(LBP)
extracted
from
epithelial
layer
classifying
OSFWD
OSFD.
For
purpose,
we
twenty
three
HOS
nine
LBP
fed
them
Support
Vector
Machine
(SVM)
automated
diagnosis.
One
hundred
fifty
eight
images
(90
42
26
OSFD
images)
were
analysis.
provide
good
sensitivity
82.85%
specificity
87.84%,
higher
values
(94.07%)
(93.33%)
using
SVM
classifier.
proposed
system,
can
be
as
an
adjunct
tool
by
cross-check
their
Algorithms,
Journal Year:
2023,
Volume and Issue:
16(12), P. 531 - 531
Published: Nov. 21, 2023
Contrast
enhancement
techniques
serve
the
purpose
of
diminishing
image
noise
and
increasing
contrast
relevant
structures.
In
context
medical
images,
where
differentiation
between
normal
abnormal
tissues
can
be
quite
subtle,
precise
interpretation
might
become
challenging
when
levels
are
relatively
elevated.
The
Fast
Local
Laplacian
Filter
(FLLF)
is
proposed
to
deliver
a
more
present
clearer
observer;
this
achieved
through
reduction
levels.
study,
FLLF
strengthened
images
its
unique
capabilities
while
preserving
important
details.
It
by
adapting
image’s
characteristics
selectively
enhancing
areas
with
low
contrast,
thereby
improving
overall
visual
quality.
Additionally,
excels
in
edge
preservation,
ensuring
that
fine
details
retained
edges
remain
sharp.
Several
performance
metrics
were
employed
assess
effectiveness
technique.
These
included
Peak
Signal-to-Noise
Ratio
(PSNR),
Mean
Squared
Error
(MSE),
Root
(RMSE),
Normalization
Coefficient
(NC),
Correlation
Coefficient.
results
indicated
technique
PSNR
40.12,
an
MSE
8.6982,
RMSE
2.9492,
NC
1.0893,
0.9999.
analysis
highlights
superior
method
applied,
especially
compared
existing
techniques.
This
approach
high-quality
minimal
information
loss,
ultimately
aiding
experts
making
accurate
diagnoses.
