A
widespread
issue
across
the
globe,
breast
cancer
impacts
women
diverse
regions
and
populations.
Early
detection
remains
crucial
for
improving
treatment
outcomes
reducing
mortality
rates
associated
with
disease.
Advancements
in
technology,
especially
Machine
Learning
(ML),
present
promising
opportunities
to
enhance
accuracy
effectiveness
of
methods.
The
research
carried
out
this
investigation
involves
a
comparative
analysis
three
ML
models
(DT,
ANN,
SVM),
utilizing
Wisconsin
Diagnostic
Breast
Cancer
(WDBC)
dataset
incorporating
ANOVA
feature
selection.
primary
objective
is
evaluate
these
achieving
precise
timely
detection.
Through
comprehensive
assessment,
which
includes
common
metrics,
our
findings
underscore
superior
performance
SVM
model,
precision,
recall,
F1-score
98.59%.
These
results
SVM's
potential
accurate
early
prediction
using
dataset.
This
contributes
advancing
understanding
machine
learning
methodologies
diagnosis,
emphasizing
significant
role
technology
facilitating
patient
outcomes.
Machine Learning and Knowledge Extraction,
Год журнала:
2024,
Номер
6(1), С. 699 - 736
Опубликована: Март 21, 2024
In
this
review,
we
compiled
convolutional
neural
network
(CNN)
methods
which
have
the
potential
to
automate
manual,
costly
and
error-prone
processing
of
medical
images.
We
attempted
provide
a
thorough
survey
improved
architectures,
popular
frameworks,
activation
functions,
ensemble
techniques,
hyperparameter
optimizations,
performance
metrics,
relevant
datasets
data
preprocessing
strategies
that
can
be
used
design
robust
CNN
models.
also
machine
learning
algorithms
for
statistical
modeling
current
literature
uncover
latent
topics,
method
gaps,
prevalent
themes
future
advancements.
The
results
indicate
temporal
shift
in
favor
designs,
such
as
from
use
architecture
CNN-transformer
hybrid.
insights
point
surge
practitioners
into
imaging
field,
partly
driven
by
COVID-19
challenge,
catalyzed
detecting
diagnosing
pathological
conditions.
This
phenomenon
likely
contributed
sharp
increase
number
publications
on
CNNs
imaging,
both
during
after
pandemic.
Overall,
existing
has
certain
gaps
scope
with
respect
optimization
architectures
specifically
imaging.
Additionally,
there
is
lack
post
hoc
explainability
models
slow
progress
adopting
low-resource
review
ends
list
open
research
questions
been
identified
through
recommendations
potentially
help
set
up
more
robust,
reproducible
experiments
BMC Medical Informatics and Decision Making,
Год журнала:
2024,
Номер
24(1)
Опубликована: Апрель 30, 2024
Brain
tumors
pose
a
significant
medical
challenge
necessitating
precise
detection
and
diagnosis,
especially
in
Magnetic
resonance
imaging(MRI).
Current
methodologies
reliant
on
traditional
image
processing
conventional
machine
learning
encounter
hurdles
accurately
discerning
tumor
regions
within
intricate
MRI
scans,
often
susceptible
to
noise
varying
quality.
The
advent
of
artificial
intelligence
(AI)
has
revolutionized
various
aspects
healthcare,
providing
innovative
solutions
for
diagnostics
treatment
strategies.
This
paper
introduces
novel
AI-driven
methodology
brain
from
images,
leveraging
the
EfficientNetB2
deep
architecture.
Our
approach
incorporates
advanced
preprocessing
techniques,
including
cropping,
equalization,
application
homomorphic
filters,
enhance
quality
data
more
accurate
detection.
proposed
model
exhibits
substantial
performance
enhancement
by
demonstrating
validation
accuracies
99.83%,
99.75%,
99.2%
BD-BrainTumor,
Brain-tumor-detection,
Brain-MRI-images-for-brain-tumor-detection
datasets
respectively,
this
research
holds
promise
refined
clinical
patient
care,
fostering
reliable
identification
images.
All
is
available
Github:
https://github.com/muskan258/Brain-Tumor-Detection-from-MRI-Images-Utilizing-EfficientNetB2
).
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.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Окт. 29, 2024
Abstract
In
the
field
of
medical
imaging,
accurately
classifying
brain
tumors
remains
a
significant
challenge
because
visual
similarities
among
different
tumor
types.
This
research
addresses
multiclass
categorization
by
employing
Support
Vector
Machine
(SVM)
as
core
classification
algorithm
and
analyzing
its
performance
in
conjunction
with
feature
extraction
techniques
such
Histogram
Oriented
Gradients
(HOG)
Local
Binary
Pattern
(LBP),
well
dimensionality
reduction
technique,
Principal
Component
Analysis
(PCA).
The
study
utilizes
dataset
sourced
from
Kaggle,
comprising
MRI
images
classified
into
four
classes,
captured
various
anatomical
planes.
Initially,
SVM
model
alone
attained
an
accuracy(acc_val)
86.57%
on
unseen
test
data,
establishing
baseline
for
performance.
To
enhance
this,
PCA
was
incorporated
reduction,
which
improved
acc_val
to
94.20%,
demonstrating
effectiveness
reducing
mitigating
overfitting
enhancing
generalization.
Further
gains
were
realized
applying
techniques—HOG
LBP—in
SVM,
resulting
95.95%.
most
substantial
improvement
observed
when
combining
both
HOG,
LBP,
PCA,
achieving
impressive
96.03%,
along
F1
score(F1_val)
96.00%,
precision(prec_val)
96.02%,
recall(rec_val)
96.03%.
approach
will
not
only
improves
but
also
efficacy
computation,
making
it
robust
effective
method
prediction.
International Journal of Online and Biomedical Engineering (iJOE),
Год журнала:
2024,
Номер
20(02), С. 78 - 94
Опубликована: Фев. 14, 2024
In
order
to
effectively
treat
skin
diseases,
an
accurate
and
prompt
diagnosis
is
required.
this
article,
a
novel
method
for
classifying
disorders
using
multimodal
classifier
presented.
The
proposed
utilizes
multiple
information
sources
enhance
the
accuracy
of
disease
classification.
It
incorporates
images
lesions
patient-specific
data.
simultaneously
classifies
diseases
by
combining
image
structured
data
inputs.
effectiveness
was
evaluated
ISIC
2018
dataset,
which
includes
clinical
seven
categories
diseases.
results
indicate
that
model
outperforms
conventional
single-modal
single-task
classifiers,
achieving
98.66%
classification
94.40%
addition,
we
compare
performance
with
other
methodologies,
demonstrating
its
superiority.
Despite
yielding
promising
results,
has
limitations
in
terms
requirements
generalizability.
Future
research
directions
include
incorporating
additional
sources,
investigating
genetic
integration,
applying
various
medical
conditions.
This
study
illustrates
potential
integrating
techniques
transfer
learning
deep
neural
networks
cutaneous