Medical
image
analysis
is
an
important
branch
in
the
field
of
medicine,
which
mainly
uses
processing
and
techniques
to
interpret
diagnose
medical
data.
data
helps
doctors
effectively
observe
patients'
body
structures,
tissues
lesions.
has
been
research
area
field,
it
for
disease
diagnosis,
treatment
planning,
condition
monitoring.
In
recent
years,
rapid
development
deep
learning
computer
vision
technologies
contributed
greatly
automation,
multimodal
fusion,
real-time
application,
accuracy
improvement
analysis.
addition,
given
rise
some
new
areas
analysis,
such
as
Generative
Adversarial
Networks
(GANs)
synthetic
images,
self-supervised
unsupervised
feature
learning,
neural
network
interpretability.
this
paper,
we
will
introduce
optimisation
methods
images
are
effective
improving
accuracy,
efficiency
reliability
Medical
image
analysis
is
an
important
branch
in
the
field
of
medicine,
which
mainly
uses
processing
and
techniques
to
interpret
diagnose
medical
data.
data
helps
doctors
effectively
observe
patients'
body
structures,
tissues
lesions.
has
been
research
area
field,
it
for
disease
diagnosis,
treatment
planning,
condition
monitoring.
In
recent
years,
rapid
development
deep
learning
computer
vision
technologies
contributed
greatly
automation,
multimodal
fusion,
real-time
application,
accuracy
improvement
analysis.
addition,
given
rise
some
new
areas
analysis,
such
as
Generative
Adversarial
Networks
(GANs)
synthetic
images,
self-supervised
unsupervised
feature
learning,
neural
network
interpretability.
this
paper,
we
will
introduce
optimisation
methods
images
are
effective
improving
accuracy,
efficiency
reliability
