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J
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en
esis
an
d
ca
u
s
e
o
f
t
h
is
ab
er
r
an
t
tu
m
o
r
g
r
o
wth
is
lim
ited
,
p
r
ev
e
n
tin
g
u
s
f
r
o
m
m
ak
in
g
d
ef
in
itiv
e
co
n
cl
u
s
io
n
s
[
1
]
.
T
u
m
o
r
s
ca
n
b
e
class
if
ied
b
ased
o
n
th
eir
p
o
ten
tial
f
o
r
m
alig
n
an
cy
an
d
th
eir
s
ite
o
f
o
r
ig
i
n
.
A
tu
m
o
r
th
at
is
n
o
t
m
alig
n
a
n
t
a
n
d
d
o
es
n
o
t
m
etastas
ize
to
o
th
e
r
o
r
g
an
s
is
class
if
ied
as
a
b
e
n
ig
n
tu
m
o
r
[
2
]
.
T
h
ese
en
titi
es
ar
e
r
ea
d
ily
n
o
ticea
b
le
an
d
e
x
h
ib
it
a
m
o
d
er
ate
p
ac
e
o
f
ex
p
an
s
io
n
.
Ma
lig
n
an
t
b
r
ai
n
tu
m
o
r
s
o
r
ig
in
atin
g
f
r
o
m
ca
n
ce
r
h
a
v
e
th
e
a
b
ilit
y
to
m
etastas
ize
to
d
if
f
er
en
t r
eg
i
o
n
s
o
f
th
e
b
r
ain
.
T
h
ese
tu
m
o
r
s
ex
h
ib
it
a
h
ig
h
lev
e
l
o
f
ag
g
r
ess
iv
en
ess
an
d
p
o
s
e
a
s
ig
n
if
ican
t
ch
allen
g
e
in
ter
m
s
o
f
d
etec
tio
n
[
3
]
.
W
h
en
s
ea
r
ch
in
g
f
o
r
a
tu
m
o
r
,
d
o
cto
r
s
em
p
lo
y
eith
er
X
-
r
ay
s
o
r
m
ag
n
etic
r
eso
n
an
ce
im
ag
in
g
(
MRI)
.
An
MRI
s
ca
n
is
in
d
icate
d
wh
e
n
all
o
th
er
test
s
h
av
e
p
r
o
v
en
i
n
ad
eq
u
ate
in
y
ield
in
g
s
atis
f
ac
to
r
y
o
u
tc
o
m
es.
MRI
s
ca
n
s
u
tili
ze
r
ad
io
wav
es
an
d
m
ag
n
ets to
g
en
e
r
ate
p
er
f
ec
t im
ag
es [
4
]
.
Prim
ar
y
b
r
ain
tu
m
o
r
s
ar
e
u
n
co
m
m
o
n
n
eo
p
lasms
th
at
ty
p
ically
o
r
ig
in
ate
in
th
e
b
r
ain
a
n
d
d
o
n
o
t
m
etastas
ize
to
o
th
er
r
e
g
io
n
s
o
f
th
e
b
o
d
y
.
Prim
ar
y
b
r
ain
tu
m
o
r
s
ca
n
b
e
eith
er
b
en
ig
n
o
r
m
ali
g
n
an
t.
T
h
e
g
r
o
wth
o
f
a
b
en
ig
n
b
r
ai
n
tu
m
o
r
is
ch
ar
ac
ter
ized
b
y
its
s
lo
w
p
ac
e,
well
-
d
ef
in
e
d
b
o
u
n
d
ar
i
es,
an
d
in
f
r
eq
u
e
n
t
d
is
s
em
in
atio
n
.
Alth
o
u
g
h
th
e
ce
lls
in
b
en
ig
n
t
u
m
o
r
s
a
r
e
n
o
t
m
alig
n
a
n
t,
th
e
y
ca
n
n
ev
er
th
eless
r
ep
r
esen
t
a
s
ig
n
if
ican
t
h
ea
lth
r
is
k
if
th
e
y
i
n
f
iltra
te
a
cr
itical
r
eg
io
n
.
Ma
lig
n
an
t
b
r
ain
tu
m
o
r
s
ex
h
ib
it
r
a
p
id
g
r
o
wth
,
p
o
s
s
ess
p
o
o
r
ly
d
ef
in
ed
b
o
u
n
d
ar
ies,
an
d
u
ltima
tely
m
etastas
ize
to
o
t
h
er
ar
ea
s
o
f
th
e
b
r
ain
.
C
o
n
tr
ar
y
to
its
wid
esp
r
ea
d
lab
el,
m
alig
n
an
t
b
r
ain
tu
m
o
r
s
d
o
n
o
t
f
u
l
f
ill
th
e
d
iag
n
o
s
tic
cr
iter
ia
f
o
r
ca
n
ce
r
s
in
ce
th
ey
d
o
n
o
t
u
n
d
er
g
o
m
etastas
i
s
,
wh
ich
is
th
e
s
p
r
ea
d
in
g
o
f
ca
n
ce
r
to
o
th
e
r
ar
ea
s
o
f
th
e
b
o
d
y
b
ey
o
n
d
th
e
b
r
ain
a
n
d
s
p
in
al
co
r
d
[
5
]
.
MRI
u
tili
ze
s
th
e
co
m
b
in
atio
n
o
f
r
ad
io
wav
es
an
d
m
a
g
n
etic
f
ield
s
to
p
r
o
v
id
e
h
ar
m
less
an
d
p
ain
less
im
ag
es
o
f
t
h
e
b
r
ain
.
MRI
d
is
tin
g
u
is
h
es
its
elf
f
r
o
m
c
o
m
p
u
ted
to
m
o
g
r
a
p
h
y
(
C
T
)
b
y
n
o
t
u
tili
zin
g
r
a
d
iatio
n
wav
es.
T
y
p
ically
,
an
MRI
s
ca
n
n
er
will
in
clu
d
e
a
lar
g
e
to
r
o
i
d
al
m
ag
n
etic
f
ield
with
a
ce
n
tr
al
ap
er
tu
r
e.
As
p
ar
t
o
f
th
e
p
r
o
ce
d
u
r
e,
ea
ch
p
atien
t
will r
ec
lin
e
o
n
a
tab
le
th
at
s
m
o
o
th
ly
m
o
v
es in
to
th
e
test
in
g
ch
an
n
el.
I
n
d
iv
id
u
als
af
f
licted
with
clau
s
tr
o
p
h
o
b
ia
m
ig
h
t
s
ee
k
s
o
lace
in
th
e
n
u
m
er
o
u
s
m
ed
ical
f
ac
ilit
ies
eq
u
ip
p
e
d
with
MRI
m
ac
h
in
es
th
at
in
clu
d
e
a
d
v
an
c
ed
o
p
en
i
n
g
tech
n
o
lo
g
y
.
Me
d
i
ca
l
f
ac
ilit
ies
an
d
im
ag
in
g
in
s
titu
tes
u
tili
ze
MRI
m
ac
h
in
es
to
co
n
d
u
ct
b
r
ai
n
ex
am
in
atio
n
s
[
6
]
.
A
h
ig
h
-
p
o
we
r
ed
an
ten
n
a
ca
n
p
r
ec
is
ely
d
eter
m
in
e
th
e
p
r
ec
is
e
p
o
s
itio
n
o
f
an
i
n
d
iv
id
u
al'
s
ato
m
s
with
in
th
e
b
o
d
y
b
y
u
tili
zi
n
g
r
ad
io
wav
es,
wh
ich
ar
e
s
u
b
s
eq
u
en
tly
s
en
t
to
a
co
m
p
u
ter
f
o
r
ex
am
in
atio
n
.
C
o
m
p
u
ter
s
h
av
e
th
e
ab
ilit
y
to
d
o
m
illi
o
n
s
o
f
c
alcu
latio
n
s
,
r
esu
ltin
g
in
th
e
g
en
er
atio
n
o
f
h
ig
h
-
r
eso
lu
tio
n
i
m
ag
es o
f
th
e
h
u
m
a
n
b
o
d
y
[
7
]
.
MRI
ca
n
d
etec
t
b
r
ain
tu
m
o
r
s
,
cy
s
ts
,
s
wellin
g
,
b
leed
in
g
,
ir
r
eg
u
lar
ities
in
ex
p
an
s
io
n
a
n
d
s
tr
u
ctu
r
es,
in
f
lam
m
ato
r
y
co
n
d
itio
n
s
,
d
is
ea
s
es,
an
d
b
lo
o
d
v
ess
el
ab
n
o
r
m
alities
.
Fo
llo
win
g
a
b
r
ai
n
in
ju
r
y
o
r
s
tr
o
k
e
,
a
d
eter
m
in
atio
n
is
m
ad
e
r
eg
ar
d
i
n
g
th
e
o
p
tim
al
tim
in
g
f
o
r
s
u
r
g
ical
in
ter
v
en
tio
n
to
id
e
n
tify
th
e
s
p
ec
if
ic
r
eg
io
n
th
at
h
as b
ee
n
im
p
ac
ted
.
MRI
s
ca
n
s
o
f
th
e
b
r
ain
ar
e
v
alu
ab
le
f
o
r
d
etec
tin
g
d
is
o
r
d
e
r
s
s
u
ch
as m
u
ltip
le
s
cler
o
s
is
,
ch
r
o
n
ic
f
atig
u
e
s
y
n
d
r
o
m
e
(
C
FS
)
,
h
ea
d
ac
h
es,
b
lu
r
r
ed
v
is
io
n
,
an
d
o
th
er
r
elate
d
p
r
o
b
lem
s
.
An
MRI
s
ca
n
is
cr
u
cial
f
o
r
d
iag
n
o
s
in
g
p
r
o
b
lem
s
with
th
e
p
itu
itar
y
g
lan
d
a
n
d
b
r
ain
s
tem
,
as
it
o
f
f
er
s
p
r
ec
is
e
im
ag
es
o
f
th
e
b
r
ain
s
tr
u
ctu
r
es th
at
ca
n
n
o
t
b
e
s
ee
n
u
s
in
g
C
T
,
X
-
r
ay
,
o
r
u
ltra
s
o
u
n
d
[
8
]
.
T
h
e
MRI
im
ag
e
o
f
b
r
ain
p
ar
t h
as b
ee
n
illu
s
tr
ated
in
Fig
u
r
e
1
.
T
h
e
M
R
I
im
ag
e
o
f
b
r
ain
tu
m
o
r
d
etec
tio
n
h
as b
ee
n
estab
lis
h
ed
in
Fig
u
r
e
2
.
Fig
u
r
e
1
.
MRI
im
ag
e
f
o
r
b
r
ain
Fig
u
r
e
2
.
MRI
im
ag
e
f
o
r
b
r
ain
tu
m
o
r
d
etec
tio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
E
n
h
a
n
ce
d
d
ee
p
a
u
to
en
c
o
d
er
tech
n
iq
u
e
fo
r
b
r
a
i
n
tu
mo
u
r
…
(
S
ye
d
Ja
h
a
n
g
ir
B
a
d
a
s
h
a
h
)
2033
I
n
th
e
f
ield
o
f
m
ed
icin
e,
d
ig
ital
im
ag
e
p
r
o
ce
s
s
in
g
h
as
th
e
ca
p
ac
ity
to
s
ig
n
if
ican
tly
as
s
is
t
in
th
e
id
en
tific
atio
n
o
f
d
is
ea
s
es
an
d
tu
m
o
r
s
.
E
v
e
r
y
p
i
x
el
in
a
d
ig
ital
im
ag
e
p
o
s
s
ess
es
a
d
is
tin
ct
i
n
ten
s
ity
v
alu
e
an
d
s
p
ec
if
ic
p
o
s
itio
n
,
co
n
s
titu
tin
g
a
d
ef
in
e
d
q
u
an
tity
o
f
elem
e
n
ts
.
MRI
is
p
ar
ticu
lar
ly
v
al
u
ab
le
in
th
e
f
ield
o
f
m
ed
icin
e
f
o
r
o
b
tain
i
n
g
a
d
ee
p
u
n
d
er
s
tan
d
in
g
o
f
th
e
in
ter
n
al
p
r
o
ce
s
s
es
o
f
th
e
b
o
d
y
[
9
]
.
T
h
e
m
ain
p
u
r
p
o
s
e
o
f
th
is
is
to
u
n
co
v
er
d
is
cr
ep
an
ci
es
in
b
o
d
ily
tis
s
u
es
th
at
ca
n
b
e
ass
ess
ed
u
tili
zin
g
an
en
h
a
n
ce
d
tech
n
iq
u
e
as
o
p
p
o
s
ed
t
o
ap
p
r
o
x
im
ate
d
to
m
o
g
r
ap
h
y
.
C
o
n
s
eq
u
en
tly
,
t
h
is
tech
n
iq
u
e
is
em
p
lo
y
ed
as
a
u
n
iq
u
e
m
eth
o
d
,
p
ar
ticu
lar
ly
f
o
r
ca
n
ce
r
im
a
g
in
g
an
d
th
e
i
d
e
n
tific
atio
n
o
f
b
r
a
in
tu
m
o
r
s
.
MRI
p
ictu
r
es
ar
e
d
i
v
id
ed
i
n
to
m
u
ltip
le
p
iece
s
u
s
in
g
a
tech
n
iq
u
e
ca
lle
d
im
ag
e
s
eg
m
en
tatio
n
.
T
h
is
a
p
p
r
o
ac
h
g
en
er
ates
n
u
m
er
o
u
s
s
ets
o
f
p
ix
els
with
in
a
s
in
g
le
im
ag
e.
I
m
ag
e
s
eg
m
en
tatio
n
ca
n
s
im
p
lify
th
e
p
r
o
ce
s
s
b
y
d
o
in
g
in
-
d
e
p
th
an
al
y
s
is
an
d
co
llectin
g
p
er
tin
en
t d
ata.
An
o
th
er
in
ter
p
r
etatio
n
is
th
e
d
is
s
em
in
atio
n
o
f
eq
u
iv
alen
t d
is
tin
ctiv
en
ess
b
y
a
s
s
ig
n
in
g
a
lab
el
to
e
a
c
h
p
i
x
e
l
i
n
a
b
r
a
i
n
i
m
a
g
e
.
T
h
e
o
u
t
c
o
m
e
s
f
r
o
m
t
h
e
p
i
x
e
l
a
n
a
l
y
s
i
s
w
i
l
l
b
e
o
f
t
e
n
u
t
i
l
i
z
e
d
f
o
r
a
s
s
e
t
d
i
s
t
r
i
b
u
t
i
o
n
[
1
0
]
.
2.
RE
L
AT
E
D
WO
RK
2
.
1
.
Seg
m
ent
a
t
i
o
n
t
ec
hn
iqu
e
s
f
o
r
bra
in
t
um
o
rs
An
ess
en
tial
s
tag
e
in
co
m
p
u
ter
-
aid
ed
d
ia
g
n
o
s
tic
tech
n
iq
u
es
is
th
e
id
en
tific
atio
n
an
d
s
u
b
s
eq
u
en
t
d
iv
is
io
n
o
f
b
r
ain
tu
m
o
r
s
;
th
is
s
tag
e
en
co
m
p
ass
es
a
m
u
ltit
u
d
e
o
f
co
n
tr
ib
u
tio
n
s
.
Au
th
o
r
s
in
tr
o
d
u
ce
d
a
n
o
v
el
ap
p
r
o
ac
h
u
tili
zin
g
d
ee
p
n
eu
r
a
l
n
etwo
r
k
s
to
ac
c
u
r
ately
s
ep
ar
ate
b
r
ain
im
ag
es
f
r
o
m
2
D
M
R
I
d
ata.
T
h
e
m
o
d
el
in
clu
d
es
an
en
co
d
er
an
d
d
e
co
d
er
th
at
ca
n
r
ec
o
g
n
ize
ca
n
ce
r
s
o
f
ten
id
e
n
tifie
d
b
y
m
e
d
ical
p
r
o
f
ess
io
n
als.
Ad
d
itio
n
ally
,
a
s
p
ec
if
ic
d
at
a
au
g
m
e
n
tatio
n
tech
n
iq
u
e
is
u
s
ed
.
L
o
w
-
g
r
ad
e
g
lio
m
a
is
co
n
s
id
er
ed
m
o
r
e
ch
allen
g
in
g
to
tr
ea
t
co
m
p
ar
ed
to
o
th
er
f
o
r
m
s
o
f
m
alig
n
an
t
o
r
tu
m
o
r
o
u
s
ce
lls
d
u
e
to
th
e
r
elativ
ely
ch
allen
g
in
g
n
atu
r
e
o
f
tu
m
o
r
d
etec
tio
n
u
s
in
g
s
tan
d
ar
d
p
r
o
ce
d
u
r
es.
T
h
e
m
o
d
el
was
f
ac
ilit
ated
b
y
au
to
m
ated
ap
p
r
o
ac
h
es
f
o
r
d
etec
tin
g
an
d
lo
ca
lizin
g
lo
w
-
g
r
ad
e
g
lio
m
as,
as
s
tated
b
y
th
e
au
th
o
r
s
.
T
h
e
m
eth
o
d
o
lo
g
y
was
v
alid
ated
u
s
in
g
th
e
p
ix
el
ac
c
u
r
ac
y
m
eth
o
d
,
w
h
ich
was
im
p
lem
en
ted
with
t
h
e
Z
n
et
f
r
am
ewo
r
k
,
a
d
ee
p
le
ar
n
in
g
a
r
ch
itectu
r
e.
A
s
em
an
tic
s
eg
m
en
tatio
n
m
eth
o
d
was
d
ev
is
ed
to
ad
d
r
ess
th
e
is
s
u
e
o
f
class
im
b
alan
ce
.
T
h
is
ap
p
r
o
ac
h
d
ep
en
d
e
d
o
n
ac
q
u
i
r
in
g
th
e
tr
u
e
v
alu
es
f
r
o
m
th
e
b
ac
k
g
r
o
u
n
d
elem
en
ts
,
wh
ich
o
f
ten
r
ep
la
ce
d
with
v
er
y
h
ig
h
p
ix
el
v
alu
es th
at
co
u
ld
b
e
m
is
lead
in
g
[
1
1
]
.
Au
th
o
r
s
p
r
o
v
id
ed
a
d
escr
ip
tio
n
o
f
a
m
eth
o
d
f
o
r
s
eg
m
en
tin
g
b
r
ain
tu
m
o
r
s
u
s
in
g
MRI
d
ata
.
B
o
th
th
e
tr
ain
in
g
MRI
p
ictu
r
es a
n
d
th
e
ass
es
s
m
en
t in
f
er
en
ce
wer
e
in
c
lu
d
ed
in
th
e
s
tr
ateg
y
.
T
h
e
lack
o
f
ef
f
ec
tiv
en
ess
o
f
cu
r
r
en
t
c
u
ttin
g
-
e
d
g
e
tec
h
n
iq
u
e
s
in
p
r
o
v
id
in
g
ac
cu
r
ate
r
esu
lts
in
r
ea
l
-
tim
e
s
itu
atio
n
s
led
t
o
t
h
e
d
ev
el
o
p
m
en
t
o
f
a
cr
o
s
s
-
m
o
d
al
d
is
till
atio
n
s
tr
ateg
y
.
T
o
o
v
er
co
m
e
t
h
e
co
n
s
tr
ain
ts
o
f
th
e
s
in
g
le
s
eq
u
en
ce
C
NN
m
o
d
el,
it
wa
s
im
p
er
ativ
e
to
d
ev
el
o
p
an
e
n
h
an
ce
d
C
NN
ar
ch
itectu
r
e
ca
p
ab
le
o
f
ef
f
ec
tiv
ely
an
aly
zi
n
g
th
e
MRI
im
ag
es,
wh
ich
co
m
m
o
n
ly
co
n
s
is
t
o
f
m
an
y
s
eq
u
en
ce
s
o
f
d
ata.
Du
r
i
n
g
th
e
ev
alu
atio
n
o
f
th
e
m
o
d
e
ls
u
s
in
g
th
e
B
R
AT
S
2
0
1
8
d
ataset,
r
esear
ch
er
s
d
is
co
v
er
ed
th
at
cr
o
s
s
-
d
is
till
atio
n
y
ield
ed
s
u
p
er
io
r
r
esu
lts
in
ter
m
s
o
f
s
eg
m
en
tatio
n
q
u
ality
c
o
m
p
ar
e
d
to
a
s
in
g
le
s
eq
u
en
ce
C
NN
m
o
d
el.
C
r
o
s
s
-
d
is
till
at
io
n
was
s
h
o
wn
to
en
h
an
ce
th
e
o
p
er
atio
n
al
m
o
d
el
o
f
a
s
in
g
le
s
eq
u
en
ce
C
NN
wh
en
ex
p
o
s
ed
to
m
u
ltip
le
s
eq
u
en
tial M
R
I
im
ag
e
d
ata
[
1
2
]
.
I
n
th
eir
s
tu
d
y
,
r
esear
c
h
er
s
in
tr
o
d
u
ce
d
a
m
u
ltit
ask
in
g
ap
p
r
o
ac
h
th
at
co
n
s
id
er
s
th
e
p
r
esen
ce
o
f
m
an
y
m
o
d
alities
in
MRI
d
ata.
T
h
e
ap
p
r
o
ac
h
was
d
escr
ib
ed
as
an
au
to
m
ated
p
r
o
ce
d
u
r
e
u
s
in
g
s
eg
m
en
tatio
n
f
u
s
io
n
,
in
wh
ich
a
v
ar
iatio
n
al
en
co
d
er
was
em
p
lo
y
ed
to
co
m
b
in
e
m
an
y
ch
ar
ac
ter
is
tics
an
d
r
eb
u
ild
th
e
im
ag
es.
A
r
eg
u
lar
izatio
n
tech
n
iq
u
e
w
as
em
p
lo
y
ed
to
ac
h
iev
e
s
eg
m
en
tatio
n
f
u
s
io
n
,
ai
m
in
g
to
e
x
tr
ac
t
f
ea
tu
r
es
f
r
o
m
m
an
y
m
o
d
alities
.
T
h
e
o
b
jectiv
e
o
f
th
e
f
u
s
io
n
was
to
en
h
an
ce
f
ea
tu
r
e
en
g
in
ee
r
in
g
b
y
ac
q
u
ir
in
g
a
m
o
r
e
co
m
p
r
eh
e
n
s
iv
e
u
n
d
er
s
tan
d
i
n
g
o
f
th
e
s
ev
er
al
ch
a
r
ac
ter
i
s
tics
p
r
esen
t
in
th
e
in
p
u
t
p
h
o
to
s
.
T
h
e
m
o
d
el
in
co
r
p
o
r
ated
a
n
u
n
ce
r
tain
ty
-
b
ased
ap
p
r
o
ac
h
in
to
its
m
u
lti
-
task
in
g
f
r
a
m
ewo
r
k
,
r
esu
ltin
g
i
n
d
ec
r
ea
s
ed
weig
h
t
d
u
r
in
g
th
e
tr
ai
n
in
g
p
r
o
ce
s
s
.
T
h
e
m
o
d
el
u
n
d
er
wen
t
test
in
g
with
r
ea
l
-
tim
e
s
ce
n
ar
io
s
u
tili
z
in
g
th
e
b
en
ch
m
ar
k
d
ataset,
B
R
AT
S
2
0
2
0
.
T
h
e
r
esu
lts
in
d
icate
th
at
th
e
m
u
lti
-
task
in
g
m
o
d
el
o
u
tp
e
r
f
o
r
m
s
th
e
v
ar
iatio
n
al
au
to
en
co
d
er
(
VAE
)
m
o
d
el
in
ter
m
s
o
f
b
o
th
co
m
p
u
tatio
n
al
e
f
f
icien
cy
an
d
ac
cu
r
ac
y
in
s
eg
m
en
tin
g
[
1
3
]
.
R
esear
ch
wo
r
k
em
p
lo
y
e
d
a
l
ea
r
n
in
g
an
d
b
r
a
n
ch
in
g
s
tr
ateg
y
to
cr
ea
te
an
HM
R
Net
m
eth
o
d
th
at
in
teg
r
ates
a
m
u
ltis
ca
lin
g
f
ea
t
u
r
e.
T
h
is
m
eth
o
d
is
s
p
ec
if
ical
ly
d
ev
elo
p
ed
to
p
r
o
ce
s
s
p
h
o
to
g
r
ap
h
s
th
at
h
av
e
a
h
ig
h
lev
el
o
f
d
etail
an
d
clar
ity
.
T
h
e
o
b
jectiv
e
o
f
th
e
m
o
d
el
was
to
ac
cu
r
ately
id
en
tif
y
an
d
s
ep
ar
ate
th
e
clin
ical
tar
g
et
v
o
lu
m
e
b
y
an
aly
zin
g
a
n
d
d
iv
i
d
in
g
im
a
g
es
o
f
th
e
an
ato
m
ical
b
r
ain
b
ar
r
ier
s
to
ca
n
ce
r
.
Ou
r
p
r
im
ar
y
em
p
h
asis
was
o
n
th
e
s
ca
les,
wh
ile
also
en
s
u
r
in
g
th
e
ac
cu
r
ate
r
etr
iev
al
an
d
p
r
eser
v
atio
n
o
f
th
e
an
ato
m
ical
ch
ar
ac
ter
is
tics
o
f
th
e
h
u
m
an
b
r
ain
as
co
n
tex
tu
al
in
f
o
r
m
atio
n
.
T
h
e
b
id
ir
ec
tio
n
al
f
ea
t
u
r
e
ca
lib
r
atio
n
was
estab
lis
h
ed
as
a
m
ea
n
s
to
tu
r
n
th
e
f
ea
tu
r
es
in
to
s
p
atial
atten
ti
o
n
m
ap
s
,
with
th
e
g
o
al
o
f
en
h
an
cin
g
th
e
f
ea
tu
r
es
an
d
f
ac
ilit
atin
g
im
p
r
o
v
e
d
s
eg
m
en
tatio
n
.
Seg
m
en
tin
g
th
e
b
r
ain
is
d
if
f
icu
lt
d
u
e
o
f
th
e
d
iv
er
s
e
s
izes
an
d
s
h
ap
es
o
f
th
e
k
n
o
wn
b
a
r
r
ier
s
.
T
h
e
d
es
cr
ib
ed
ap
p
r
o
ac
h
b
o
o
s
ted
b
o
th
b
id
ir
ec
tio
n
al
an
d
m
o
n
o
d
i
r
ec
tio
n
al
ca
p
ab
ilit
ies b
y
ex
ten
d
in
g
th
e
atten
tio
n
s
p
an
,
wh
ile
h
ig
h
-
r
eso
lu
tio
n
im
ag
es e
n
h
an
ce
d
th
e
s
tr
u
ctu
r
al
in
f
o
r
m
atio
n
.
T
h
is
m
o
d
el
is
d
ep
en
d
e
n
t o
n
ex
clu
s
iv
ely
AB
C
d
ata
o
b
tain
ed
f
r
o
m
th
e
MI
C
C
AI
d
ataset
[
1
4
]
.
I
n
v
en
to
r
s
d
em
o
n
s
tr
ated
th
e
v
alid
ity
o
f
th
e
c
o
n
ce
p
t
o
f
m
a
n
y
m
o
d
alities
b
y
e
m
p
lo
y
i
n
g
a
s
tr
ea
m
lin
ed
ap
p
r
o
ac
h
,
n
am
ely
th
e
E
L
U
-
N
et
with
an
en
h
an
ce
d
s
tr
u
ctu
r
e
.
T
h
e
m
o
d
el
u
tili
ze
d
a
co
m
p
l
ex
s
k
ip
co
n
n
ec
tio
n
s
tr
u
ctu
r
e
to
in
v
esti
g
ate
th
e
lin
k
ag
es
th
at
a
r
o
s
e
f
r
o
m
th
e
s
em
an
tic
ass
o
ciatio
n
s
o
f
th
e
c
h
ar
a
cter
is
tics
.
T
h
e
th
ick
Evaluation Warning : The document was created with Spire.PDF for Python.
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an
d
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m
o
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p
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b
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th
eir
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p
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ic
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eg
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m
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d
th
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.
T
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o
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tr
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m
o
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h
ad
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f
o
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e
n
t
i
o
n
a
l
U
-
N
e
t
a
n
d
U
-
N
e
t
+
+
a
r
c
h
i
t
e
c
t
u
r
e
s
w
h
e
n
e
v
a
l
u
a
t
e
d
o
n
t
h
e
B
R
A
T
S
2
0
1
8
d
a
t
a
s
e
t
[
1
5
]
.
2
.
2
.
Dee
p lea
rning
a
lg
o
rit
hm
s
Dee
p
n
eu
r
al
n
etwo
r
k
s
h
a
v
e
p
r
o
v
e
n
to
b
e
h
ig
h
l
y
v
alu
a
b
le
f
o
r
m
e
d
ical
im
ag
e
s
eg
m
en
ta
tio
n
in
th
e
co
n
tex
t
o
f
co
m
p
u
ter
-
aid
e
d
d
ia
g
n
o
s
is
.
I
n
th
eir
s
tu
d
y
,
r
esear
ch
er
s
co
m
b
in
ed
d
ee
p
n
eu
r
al
n
et
wo
r
k
s
with
d
is
cr
ete
wav
elet
tr
an
s
f
o
r
m
to
d
ev
elo
p
a
m
o
d
el
th
at
ca
n
ac
cu
r
ately
d
iv
id
e
b
r
ain
tu
m
o
r
s
in
to
s
eg
m
en
ts
.
T
h
e
m
o
d
el
ca
teg
o
r
ized
b
r
ain
tu
m
o
r
s
i
n
to
th
r
ee
class
es:
s
ar
co
m
a,
m
etastatic,
an
d
g
lio
b
last
o
m
a,
e
m
p
lo
y
in
g
a
u
n
iq
u
e
m
eth
o
d
o
l
o
g
y
to
a
d
d
r
ess
th
e
p
r
o
b
lem
s
tatem
en
t.
C
o
n
s
eq
u
e
n
tly
,
th
e
m
o
d
el
o
n
ly
p
r
esen
te
d
th
e
class
if
icatio
n
r
esu
lts
f
o
r
th
e
m
alig
n
an
t
b
r
ai
n
tu
m
o
r
v
ar
iatio
n
s
.
As
p
r
ev
i
o
u
s
ly
s
tated
,
C
NNs
h
av
e
d
em
o
n
s
tr
ated
ex
ce
p
tio
n
al
p
er
f
o
r
m
an
ce
in
th
e
ar
ea
s
o
f
f
e
atu
r
e
en
g
in
ee
r
i
n
g
,
ex
t
r
ac
tio
n
,
an
d
p
ar
ticu
lar
ly
b
r
ain
tu
m
o
r
s
eg
m
en
tatio
n
.
I
n
th
e
f
u
tu
r
e,
it is
an
ticip
ated
t
h
at
th
e
s
e
n
etwo
r
k
s
will tr
an
s
itio
n
to
b
ein
g
f
u
lly
co
n
v
o
lu
tio
n
al
n
et
wo
r
k
s
[
1
6
]
.
R
esear
ch
er
s
co
n
d
u
cted
a
c
o
m
p
r
eh
en
s
iv
e
an
aly
s
is
to
e
v
alu
at
e
th
e
b
en
ef
its
o
f
d
ee
p
lear
n
in
g
m
o
d
els.
T
h
ey
co
m
p
ar
ed
th
e
p
er
f
o
r
m
an
ce
o
f
th
ese
m
o
d
els to
th
at
o
f
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
in
tr
o
d
u
ce
d
with
in
th
e
p
ast
d
ec
ad
e.
C
NNs
will
b
e
u
s
ed
to
s
eg
m
en
t,
e
x
tr
ac
t,
an
d
m
ap
b
o
u
n
d
ar
ies
f
r
o
m
in
p
u
t
im
a
g
es
th
at
h
av
e
b
ee
n
p
r
ep
ar
e
d
f
o
r
v
ar
io
u
s
p
r
o
p
er
ties
.
Sev
er
al
m
o
d
els
co
m
b
in
e
r
a
d
iatio
n
a
n
d
d
ee
p
lear
n
in
g
tec
h
n
iq
u
es
t
o
d
em
o
n
s
tr
ate
th
e
v
ar
io
u
s
p
r
o
ce
s
s
es
a
p
atien
t
wo
u
ld
u
n
d
er
g
o
.
T
h
e
r
o
ad
m
a
p
is
s
ee
n
as
a
cr
u
cial
in
s
tr
u
m
en
t
f
o
r
f
u
tu
r
e
p
lan
n
i
n
g
,
co
n
tin
g
en
t
u
p
o
n
th
e
d
u
r
atio
n
o
f
th
e
p
r
o
ce
d
u
r
e.
T
o
f
o
s
ter
ea
g
er
co
llab
o
r
atio
n
in
r
elatio
n
to
co
m
p
r
eh
e
n
s
iv
e
ap
p
licatio
n
s
,
f
o
r
th
co
m
in
g
m
o
d
els will p
o
s
s
ess
a
b
r
o
ad
er
p
e
r
s
p
ec
tiv
e.
I
n
2
0
1
7
,
th
is
r
esear
ch
w
o
r
k
c
r
ea
ted
a
s
eg
m
en
tatio
n
tech
n
iq
u
e
u
s
in
g
C
NNs.
T
h
is
tech
n
iq
u
e
co
n
s
id
er
s
b
o
th
th
e
lo
ca
l
an
d
g
lo
b
al
p
r
o
p
er
ties
o
f
d
ee
p
lear
n
in
g
f
r
am
e
wo
r
k
s
.
T
h
e
p
r
esen
ce
o
f
b
r
ain
tu
m
o
r
s
was
d
etec
ted
th
r
o
u
g
h
a
th
o
r
o
u
g
h
ex
am
in
at
io
n
o
f
th
e
im
ag
e,
f
o
cu
s
in
g
o
n
in
tr
icate
l
o
ca
l
an
d
g
lo
b
al
c
h
ar
ac
ter
is
tics
.
T
h
e
u
n
d
er
s
tan
d
i
n
g
o
f
th
e
n
ec
ess
ity
o
f
s
eg
m
en
tatio
n
an
d
th
e
o
p
ti
m
izatio
n
m
eth
o
d
s
f
o
r
it
wer
e
en
h
an
ce
d
d
u
e
to
t
h
e
v
ar
iatio
n
s
s
ee
n
am
o
n
g
th
e
attr
i
b
u
tes [
1
7
]
.
A
g
r
o
u
p
o
f
r
esear
ch
er
s
co
n
d
u
cted
a
co
m
p
r
eh
en
s
iv
e
a
n
aly
s
is
o
f
d
if
f
er
e
n
t
d
ee
p
lear
n
in
g
s
tr
ateg
ies
to
d
eter
m
in
e
th
eir
e
f
f
ec
tiv
en
es
s
.
W
e
co
n
d
u
cted
a
co
m
p
r
e
h
en
s
iv
e
an
aly
s
is
an
d
d
o
cu
m
en
tatio
n
o
f
m
an
y
co
m
p
u
ter
-
aid
ed
d
ia
g
n
o
s
is
m
o
d
els
th
at
em
p
lo
y
d
ee
p
lear
n
i
n
g
tech
n
iq
u
es.
T
h
ese
m
o
d
els
en
co
m
p
ass
a
wid
e
r
an
g
e
o
f
a
p
p
licatio
n
s
,
s
u
ch
as
d
etec
tin
g
lesi
o
n
s
,
a
n
aly
zin
g
ce
llu
lar
an
d
tis
s
u
e
o
r
g
an
izatio
n
,
ex
am
i
n
in
g
b
r
ain
h
em
is
p
h
er
es,
p
e
r
f
o
r
m
in
g
s
eg
m
en
tatio
n
,
a
n
d
class
if
y
in
g
t
u
m
o
r
s
,
am
o
n
g
o
th
er
s
.
T
h
e
ch
allen
g
es
o
f
d
ee
p
lear
n
in
g
s
tr
ateg
ies
an
d
th
e
m
e
th
o
d
s
to
o
v
er
co
m
e
th
em
wer
e
d
etailed
in
o
r
d
e
r
to
p
r
o
v
id
e
g
u
id
an
ce
f
o
r
f
u
tu
r
e
d
ev
elo
p
m
e
n
ts
[
1
8
]
.
R
esear
ch
wo
r
k
[
1
9
]
p
r
o
p
o
s
ed
a
s
tr
ateg
y
th
at
in
teg
r
ated
class
if
icatio
n
an
d
s
eg
m
e
n
tatio
n
to
p
r
ed
ict
th
e
r
eg
io
n
s
af
f
ec
ted
b
y
tu
m
o
r
s
in
th
e
g
iv
en
b
r
ai
n
im
ag
es.
T
h
e
in
p
u
t
p
h
o
to
s
wer
e
an
aly
ze
d
u
s
in
g
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
an
d
p
o
in
tin
g
k
er
n
el
c
lass
if
ier
to
d
eter
m
in
e
th
e
s
p
ec
if
ic
r
eg
io
n
s
o
f
th
e
b
r
ain
im
p
ac
ted
b
y
tu
m
o
r
ce
lls
.
I
n
o
r
d
er
to
ass
ess
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
m
o
d
el
i
n
d
icate
d
a
b
o
v
e,
we
e
v
alu
ated
its
s
en
s
itiv
ity
,
s
p
ec
if
icity
,
an
d
ac
cu
r
ac
y
.
T
h
e
p
o
i
n
tin
g
k
er
n
el
class
if
ier
d
em
o
n
s
tr
ated
s
u
p
er
io
r
p
e
r
f
o
r
m
an
ce
in
s
eg
m
e
n
tatio
n
c
o
m
p
ar
e
d
to
th
e
s
u
p
p
o
r
t v
ec
t
o
r
m
ac
h
in
e
.
3.
P
RO
P
O
SE
D
M
E
T
H
O
D
T
h
is
s
ec
tio
n
p
r
esen
ts
an
en
h
a
n
ce
d
d
ee
p
au
to
en
co
d
er
tech
n
iq
u
e
f
o
r
b
r
ain
tu
m
o
r
class
if
icatio
n
an
d
d
etec
tio
n
as
s
h
o
wn
in
Fig
u
r
e
3
.
MRI
im
ag
es
a
r
e
ac
q
u
ir
ed
f
r
o
m
B
r
aT
S
d
ata
s
ets.
I
m
a
g
es
a
r
e
p
r
e
p
r
o
ce
s
s
ed
b
y
ad
ap
tiv
e
wien
er
f
ilter
.
T
h
is
im
ag
e
p
r
ep
r
o
ce
s
s
in
g
is
n
ec
ess
ar
y
to
r
em
o
v
e
n
o
is
e
f
r
o
m
th
e
i
n
p
u
t M
R
I
im
ag
es a
n
d
r
esu
lts
in
im
p
r
o
v
in
g
ac
cu
r
ac
y
o
f
MRI
im
ag
e
class
if
ica
tio
n
.
I
m
ag
e
s
eg
m
en
tatio
n
is
p
er
f
o
r
m
ed
b
y
f
u
zz
y
C
m
ea
n
s
alg
o
r
ith
m
.
C
lass
if
icat
io
n
m
o
d
el
co
n
s
is
ts
o
f
d
ee
p
au
to
en
co
d
e
r
,
C
NN
an
d
KNN
al
g
o
r
i
t
h
m
s
.
C
l
as
s
i
f
i
c
at
i
o
n
m
o
d
e
l
is
t
r
a
i
n
e
d
a
n
d
t
e
s
t
e
d
u
s
i
n
g
B
r
aT
S
d
a
ta
s
et
M
R
I
i
m
a
g
e
s
li
c
es
.
T
h
e
f
u
n
d
am
en
tal
o
b
jectiv
e
o
f
im
ag
e
p
r
ep
r
o
ce
s
s
in
g
in
MRI
is
to
en
h
an
ce
im
ag
e
q
u
ality
f
o
r
f
o
llo
win
g
task
s
b
y
r
em
o
v
in
g
ex
tr
a
n
eo
u
s
an
d
u
n
r
elate
d
f
ea
tu
r
es
f
r
o
m
th
e
b
ac
k
g
r
o
u
n
d
.
T
h
er
e
f
o
r
e,
MRI
b
r
ain
p
ictu
r
es
ca
n
b
e
im
p
r
o
v
ed
u
s
in
g
im
ag
e
p
r
ep
r
o
ce
s
s
in
g
tech
n
iq
u
es th
at
r
e
m
o
v
e
ar
tifa
cts,
b
o
o
s
t th
e
p
ictu
r
e
q
u
ality
,
an
d
en
ab
le
m
o
r
e
p
r
ec
is
e
tu
m
o
r
s
eg
m
en
tat
io
n
.
Du
r
in
g
p
ictu
r
e
p
r
ep
r
o
ce
s
s
in
g
,
m
ask
s
ar
e
co
n
s
tr
u
cted
to
i
d
en
tify
p
ix
els with
m
ax
im
u
m
i
n
ten
s
ity
,
aim
in
g
t
o
m
in
im
ize
d
is
to
r
tio
n
.
An
ad
ap
tiv
e
wien
er
f
ilter
(
AW
F)
is
a
ty
p
e
o
f
f
ilter
th
at
o
p
er
ates
in
th
e
f
r
eq
u
e
n
cy
d
o
m
ain
.
T
h
e
AW
F
ca
n
d
y
n
am
ically
m
o
d
if
y
its
o
p
er
atio
n
b
y
tak
i
n
g
in
to
ac
co
u
n
t
th
e
m
ath
em
atica
l
s
in
g
u
lar
ity
o
f
th
e
m
ed
ical
MRI
im
ag
e
with
in
th
e
1
4
-
ar
ea
f
ilter
.
T
h
is
f
ilter
is
s
p
ec
if
ically
d
esig
n
ed
f
o
r
p
r
o
ce
s
s
in
g
MRI
im
ag
es
in
g
r
ay
-
s
ca
le
f
o
r
m
at.
T
r
an
s
f
o
r
m
atio
n
a
p
p
ly
in
g
f
ilt
er
in
g
tech
n
iq
u
es
to
r
em
o
v
e
n
o
is
e
[
2
0
]
.
I
m
ag
e
en
h
an
ce
m
en
t
p
ar
titi
o
n
in
g
b
y
u
s
e
th
e
o
u
ter
m
o
s
t
r
ec
tan
g
u
lar
f
r
a
m
e.
Ad
ap
tiv
e
f
ilter
p
r
esen
tatio
n
s
ca
n
b
e
s
u
p
er
io
r
wh
en
co
m
p
ar
e
d
to
n
o
n
-
a
d
ap
ti
v
e
co
u
n
ter
p
a
r
ts
.
Ad
ap
tiv
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f
ilter
s
will
f
ac
i
litate
th
e
esti
m
ate
o
f
two
cr
u
cial
m
ath
e
m
atica
l
v
ar
iab
les:
m
ea
n
a
n
d
v
ar
ian
ce
.
T
h
e
WF
em
p
l
o
y
s
a
p
ix
el
-
wis
e
ad
ap
tiv
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
E
n
h
a
n
ce
d
d
ee
p
a
u
to
en
c
o
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er
tech
n
iq
u
e
fo
r
b
r
a
i
n
tu
mo
u
r
…
(
S
ye
d
Ja
h
a
n
g
ir
B
a
d
a
s
h
a
h
)
2035
W
ien
er
m
eth
o
d
,
wh
ich
r
elies
o
n
s
tatis
tics
d
er
iv
ed
f
r
o
m
th
e
n
eig
h
b
o
r
in
g
p
ix
els
in
an
MRI
im
ag
e.
T
h
is
f
ilter
u
s
e
p
ix
el
-
wis
e
ad
ap
tiv
e
W
ien
er
f
ilter
in
g
to
co
n
d
u
ct
a
lo
ca
l
ass
es
s
m
en
t
o
f
m
ea
n
an
d
s
tan
d
ar
d
d
ev
iatio
n
o
n
an
MRI
im
ag
e.
I
t le
v
er
ag
es
n
eig
h
b
o
r
h
o
o
d
s
o
f
d
im
en
s
io
n
s
M
b
y
N.
C
lu
s
ter
in
g
alg
o
r
ith
m
s
ca
n
b
e
em
p
lo
y
ed
as
an
u
n
s
u
p
er
v
is
ed
s
eg
m
en
tatio
n
s
tr
ateg
y
t
o
d
iv
id
e
an
MRI
im
ag
e
in
to
m
an
y
g
r
o
u
p
s
o
f
p
i
x
els
with
co
m
p
ar
a
b
le
b
r
ig
h
tn
ess
,
with
o
u
t
th
e
n
ee
d
f
o
r
tr
ain
in
g
im
ag
es.
Du
r
in
g
th
e
tr
ain
in
g
p
r
o
ce
s
s
,
clu
s
ter
in
g
-
b
ased
s
eg
m
en
tatio
n
ap
p
r
o
ac
h
es
u
tili
ze
th
e
e
x
is
tin
g
im
ag
e
d
ata.
T
h
e
s
eg
m
en
tatio
n
alg
o
r
ith
m
s
e
m
p
lo
y
a
two
-
s
tep
p
r
o
ce
s
s
,
co
n
s
is
tin
g
o
f
d
ata
clu
s
ter
in
g
an
d
e
v
alu
atio
n
o
f
in
d
iv
id
u
al
b
r
ain
tis
s
u
e
class
es,
to
p
er
f
o
r
m
s
eg
m
en
tatio
n
a
n
d
tr
ain
in
g
s
im
u
ltan
eo
u
s
ly
.
T
h
e
m
o
s
t
co
m
m
o
n
ly
em
p
lo
y
ed
alg
o
r
ith
m
s
f
o
r
p
h
o
t
o
s
eg
m
en
tatio
n
p
r
o
b
lem
s
ar
e
K
-
m
ea
n
s
clu
s
ter
in
g
an
d
f
u
zz
y
C
-
m
ea
n
s
clu
s
ter
in
g
.
T
h
e
in
p
u
t
d
ata
ca
n
b
e
p
ar
titi
o
n
ed
in
to
K
cl
u
s
ter
s
u
s
in
g
th
e
K
-
m
ea
n
s
clu
s
ter
in
g
m
eth
o
d
[
2
1
]
.
T
h
e
m
ea
n
in
ten
s
ity
o
f
ea
ch
class
i
s
r
ep
ea
ted
ly
ass
es
s
ed
,
an
d
th
e
im
ag
e
is
s
eg
m
en
ted
b
y
class
if
y
in
g
all
p
ix
els
in
th
at
class
b
ased
o
n
th
e
ce
n
tr
o
i
d
in
th
eir
s
u
r
r
o
u
n
d
in
g
s
.
I
n
t
h
e
co
n
tex
t
o
f
b
r
ain
p
ictu
r
es,
th
is
p
r
o
ce
d
u
r
e
is
iter
ated
.
K
-
m
ea
n
s
clu
s
ter
in
g
is
co
n
s
id
e
r
ed
a
to
u
g
h
class
if
icatio
n
ap
p
r
o
ac
h
s
in
ce
it
ass
ig
n
s
ea
ch
p
ix
e
l
in
an
in
p
u
t
im
ag
e
to
a
s
in
g
le
clas
s
in
ea
ch
it
er
atio
n
.
T
h
e
f
u
zz
y
C
-
m
ea
n
s
clu
s
ter
in
g
alg
o
r
ith
m
,
also
k
n
o
wn
as
th
e
s
o
f
t
ca
teg
o
r
izatio
n
tech
n
iq
u
e,
is
a
n
o
th
er
clu
s
ter
in
g
ap
p
r
o
ac
h
th
at
is
b
ased
o
n
f
u
zz
y
s
et
th
eo
r
y
.
F
u
zz
y
C
-
m
ea
n
s
allo
ws
f
o
r
t
h
e
class
if
icatio
n
o
f
ea
ch
p
ix
el
in
to
m
an
y
g
r
o
u
p
s
b
ased
o
n
a
p
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ed
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m
in
e
d
m
em
b
er
s
h
ip
v
alu
e,
wh
ich
ca
n
b
e
s
ee
n
as a
p
o
ten
ti
al
s
im
p
lific
atio
n
o
f
K
-
m
ea
n
s
clu
s
ter
in
g
[
2
2
]
.
Fig
u
r
e
3
.
E
n
h
an
ce
d
au
to
en
co
d
er
en
ab
le
d
d
ee
p
lear
n
in
g
f
o
r
b
r
ain
tu
m
o
r
class
if
icatio
n
an
d
d
etec
tio
n
Fro
m
a
m
ed
ical
p
er
s
p
ec
tiv
e,
m
ed
ical
im
ag
es
ca
n
b
e
class
if
ied
b
ased
o
n
th
eir
d
is
tin
ctiv
e
f
ea
tu
r
es
u
tili
zin
g
th
e
im
ag
e
class
if
icat
io
n
m
eth
o
d
.
T
h
is
p
ictu
r
e
clas
s
if
icatio
n
m
eth
o
d
u
tili
ze
s
s
p
ec
tr
al
f
o
u
n
d
atio
n
o
r
s
p
ec
tr
ally
d
is
tin
ct
ch
ar
ac
ter
i
s
tics
,
s
u
ch
as
b
r
ain
d
en
s
it
y
,
tex
tu
r
e,
a
n
d
f
ea
tu
r
e
s
p
a
ce
.
R
esear
ch
h
as
d
em
o
n
s
tr
ated
th
at
im
ag
e
clas
s
if
icatio
n
tech
n
iq
u
es
ca
n
em
p
lo
y
d
ec
is
io
n
r
u
les
to
p
ar
titi
o
n
th
e
f
ea
tu
r
e
s
p
ac
e
in
to
m
an
y
class
es.
I
n
th
e
m
e
d
ical
d
o
m
ain
,
r
ap
id
c
o
m
p
u
ter
s
y
s
tem
s
,
s
u
p
p
o
r
ted
b
y
m
ath
em
atica
l
alg
o
r
ith
m
s
an
d
ef
f
ec
tiv
e
class
if
icatio
n
ap
p
r
o
ac
h
es,
h
a
v
e
s
u
cc
ess
f
u
lly
a
ch
iev
ed
p
ictu
r
e
ca
teg
o
r
izatio
n
.
T
h
e
f
o
llo
win
g
ar
e
th
e
s
tan
d
ar
d
p
r
o
ce
d
u
r
es f
o
r
ca
teg
o
r
izin
g
MRI
im
ag
es to
i
d
en
tify
tu
m
o
r
s
.
DAE
,
a
ty
p
e
o
f
d
ee
p
lear
n
in
g
tech
n
iq
u
e
,
em
p
l
o
y
s
g
r
ee
d
y
p
r
ep
ar
am
eter
o
p
tim
izatio
n
tr
ai
n
in
g
o
f
a
n
u
n
s
u
p
er
v
is
ed
lay
er
t
o
ex
tr
ac
t
t
h
e
lay
er
e
d
d
is
tin
ctiv
en
ess
o
f
h
ig
h
-
d
im
en
s
io
n
al
in
p
u
t
d
ata
f
r
o
m
m
ed
ical
im
a
g
es.
T
h
e
ap
p
r
o
ac
h
u
tili
ze
s
a
n
eu
r
a
l
n
etwo
r
k
ar
ch
itectu
r
e
a
n
d
a
m
u
ltil
ay
er
n
o
n
lin
ea
r
n
etwo
r
k
.
Data
an
aly
s
is
an
d
ex
p
lo
r
atio
n
(
DAE
)
u
tili
ze
s
t
h
is
ch
ar
ac
ter
is
tic,
an
d
th
e
d
is
tin
ctiv
e
attr
ib
u
te
o
f
s
ca
tter
ed
d
ata
is
alr
ea
d
y
ac
ce
s
s
ib
le.
T
h
e
h
id
d
e
n
lay
e
r
p
r
o
ce
s
s
,
en
c
o
d
er
,
an
d
d
ec
o
d
er
ar
e
all
co
n
tain
ed
with
in
th
e
DAE
.
T
h
e
ar
ch
itectu
r
e
an
d
t
r
ain
in
g
tech
n
iq
u
es
o
f
th
e
in
itial
au
to
m
ati
c
en
co
d
er
wer
e
e
n
h
an
ce
d
to
co
n
s
tr
u
ct
th
e
DAE
.
Var
io
u
s
DAE
s
ar
e
f
o
u
n
d
ed
o
n
d
is
tin
ct
th
eo
r
ies
with
in
th
e
r
ea
lm
o
f
d
ee
p
lear
n
in
g
,
s
u
c
h
a
s
ar
ith
m
etic
th
eo
r
y
,
s
p
ar
s
e
h
y
p
o
th
esis
,
co
n
v
o
l
u
tio
n
th
eo
r
y
,
a
n
d
r
o
b
u
s
t
th
eo
r
y
.
T
h
e
co
n
s
tr
u
ctio
n
an
d
im
p
lem
en
tatio
n
o
f
DAE
will
r
esu
lt
in
a
d
ec
r
ea
s
e
in
th
e
o
r
ig
in
al
in
p
u
t
d
im
e
n
s
io
n
s
.
B
y
an
aly
zin
g
th
e
f
u
n
d
a
m
en
tal
ch
ar
ac
ter
is
tics
o
f
th
e
in
p
u
t im
ag
e
d
ata
f
r
o
m
u
n
d
er
r
e
p
r
esen
ted
class
ty
p
ical
ex
am
p
l
es a
n
d
v
ar
io
u
s
n
o
n
-
class
b
en
c
h
m
ar
k
d
atasets
,
th
e
ef
f
icien
cy
o
f
f
ea
tu
r
e
ex
tr
ac
tio
n
is
en
h
an
ce
d
[
2
3
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
38
,
No
.
3
,
J
u
n
e
20
25
:
2
0
3
1
-
204
0
2036
A
C
NN
,
d
er
iv
ed
f
r
o
m
th
e
an
a
ly
s
is
o
f
th
e
v
is
u
al
s
y
s
tem
'
s
s
tr
u
ctu
r
e,
is
a
h
ig
h
ly
ef
f
icien
t
m
eth
o
d
f
o
r
tr
ain
in
g
a
m
u
ltil
ay
er
n
etwo
r
k
.
T
h
is
tech
n
iq
u
e
em
p
lo
y
s
s
p
atial
lin
k
s
to
m
in
im
ize
th
e
am
o
u
n
t
o
f
f
ac
to
r
s
r
eq
u
ir
ed
to
en
h
a
n
ce
th
e
tr
ain
i
n
g
p
er
f
o
r
m
an
ce
.
A
C
NN
ca
n
b
e
u
n
d
e
r
s
to
o
d
as
a
n
eu
r
al
n
e
two
r
k
with
s
ev
er
al
lay
er
s
,
wh
er
e
ea
ch
lay
er
co
n
s
is
t
s
o
f
two
-
d
im
en
s
io
n
al
p
la
n
es
an
d
ea
ch
p
lan
e
is
ch
ar
ac
ter
ized
b
y
a
s
et
o
f
in
d
iv
id
u
al
n
eu
r
o
n
s
.
An
ex
am
p
le
o
f
h
o
w
C
NN
ca
n
b
e
u
s
e
d
is
f
o
r
th
e
d
etec
tio
n
o
f
h
an
d
wr
itten
ch
ar
ac
ter
s
.
C
NNs
ar
e
m
o
s
tly
ef
f
ec
tiv
e
i
n
d
etec
tin
g
d
is
p
lace
m
en
t,
s
ca
li
n
g
,
a
n
d
o
th
er
f
o
r
m
s
o
f
d
ef
o
r
m
atio
n
in
v
ar
ian
ce
i
n
two
-
d
im
en
s
io
n
al
p
ictu
r
es.
T
h
is
s
o
lu
tio
n
av
o
id
s
th
e
e
x
p
licit
f
ea
tu
r
e
s
am
p
lin
g
m
eth
o
d
b
y
d
ir
ec
tly
ex
am
i
n
in
g
th
e
tr
ain
in
g
d
ata.
C
NNs
ca
n
g
en
er
ate
o
u
tco
m
es
th
at
v
a
r
y
s
ig
n
if
ican
tly
f
r
o
m
o
th
e
r
n
eu
r
al
n
etwo
r
k
class
if
ier
s
d
u
e
to
th
eir
co
n
s
tr
u
ctio
n
th
r
o
u
g
h
p
a
r
allel
r
esear
ch
.
T
h
is
p
ar
allel
ap
p
r
o
ac
h
h
as
th
e
p
o
ten
tial
to
p
r
o
d
u
ce
s
u
p
er
io
r
r
esu
lts
co
m
p
ar
e
d
to
n
etwo
r
k
s
co
m
p
o
s
ed
o
f
in
ter
co
n
n
ec
ted
n
eu
r
o
n
s
.
C
NNs
o
f
f
er
d
is
tin
ct
b
en
ef
its
f
o
r
im
ag
e
p
r
o
ce
s
s
in
g
an
d
s
eg
m
e
n
tatio
n
,
an
d
th
eir
ab
ilit
y
t
o
ex
tr
ac
t
v
alu
a
b
le
f
ea
tu
r
es
h
as
r
esu
lted
in
h
ig
h
l
y
ef
f
ec
tiv
e
o
u
tco
m
es.
E
v
er
y
n
eu
r
o
n
in
a
C
NN
p
o
s
s
es
s
es
a
h
ier
ar
ch
ical
lo
ca
l
ass
o
ci
atio
n
s
tr
u
ctu
r
e
an
d
n
ec
ess
itates
a
m
in
u
s
cu
le
in
p
u
t.
T
h
is
tak
es
th
e
co
m
p
u
tatio
n
al
m
o
d
el
o
f
b
io
lo
g
ical
n
eu
r
al
n
etwo
r
k
s
clo
s
er
to
th
e
d
ef
in
itiv
e
n
eu
r
al
n
etwo
r
k
.
T
h
is
tech
n
iq
u
e
will
b
e
em
p
l
o
y
ed
f
o
r
im
m
ed
iately
p
r
o
ce
s
s
in
g
m
ed
ical
p
h
o
to
s
u
s
in
g
th
e
ca
teg
o
r
izatio
n
p
r
o
ce
s
s
.
I
t sp
ec
if
ically
f
o
cu
s
es o
n
g
r
ay
lev
el
im
ag
es [
2
4
]
.
KNN
is
a
s
tr
aig
h
tf
o
r
war
d
an
d
ef
f
icien
t
n
o
n
-
p
a
r
am
etr
ic
tec
h
n
iq
u
e
u
s
ed
to
ca
teg
o
r
ize
d
ata
r
ec
o
r
d
s
.
I
t
wo
r
k
s
b
y
id
en
tify
in
g
th
e
K
NN
to
a
g
iv
en
d
ata
p
o
in
t,
wh
ich
ar
e
th
en
u
s
ed
to
cr
ea
te
a
n
eig
h
b
o
r
h
o
o
d
ar
o
u
n
d
th
at
p
o
in
t.
Usu
ally
,
wh
en
d
eter
m
in
in
g
h
o
w
to
class
if
y
t,
wh
eth
er
o
r
n
o
t d
is
tan
ce
-
b
ased
weig
h
tin
g
is
tak
en
in
to
ac
co
u
n
t,
th
e
m
o
s
t
co
m
m
o
n
ch
o
ice
is
to
r
ely
o
n
t
h
e
m
ajo
r
ity
v
o
te
o
f
n
eig
h
b
o
r
i
n
g
d
ata
en
tr
i
es.
T
h
e
ef
f
icac
y
o
f
th
e
class
if
icatio
n
h
in
g
es
o
n
th
e
s
elec
tio
n
r
ate,
s
o
th
e
ac
c
u
r
at
e
d
eter
m
in
atio
n
o
f
th
e
v
alu
e
f
o
r
k
is
cr
u
cial
w
h
en
em
p
lo
y
in
g
KNN.
T
h
e
KNN
tech
n
iq
u
e
ex
h
ib
its
b
ias
b
y
a
f
ac
to
r
o
f
k
.
W
h
en
d
ea
lin
g
with
f
u
n
d
am
en
tal
alg
o
r
ith
m
s
,
th
er
e
ar
e
n
u
m
e
r
o
u
s
m
eth
o
d
s
av
ailab
le
to
s
elec
t
th
e
m
o
s
t
ef
f
icien
t
ex
ec
u
tio
n
f
o
r
v
ar
io
u
s
v
alu
es
o
f
k
.
KNN
is
a
ca
s
e
-
b
ased
lear
n
in
g
alg
o
r
ith
m
th
at
k
ee
p
s
all
tr
ain
in
g
d
ata
f
o
r
g
r
o
u
p
i
n
g
.
I
ts
s
lo
w
lear
n
in
g
m
eth
o
d
o
l
o
g
y
d
is
q
u
alif
ies
it
f
r
o
m
n
u
m
er
o
u
s
p
o
s
s
ib
le
ap
p
lica
tio
n
s
,
s
u
ch
as
r
ea
l
-
tim
e
we
b
s
cr
ap
in
g
f
o
r
a
lar
g
e
-
s
ca
le
s
h
o
p
.
An
ap
p
r
o
ac
h
to
e
n
h
an
ce
th
e
id
en
tific
atio
n
o
f
r
ep
r
esen
tativ
es
f
o
r
co
m
m
u
n
ica
tio
n
with
all
o
f
th
e
tr
ain
in
g
class
if
icatio
n
d
ata,
n
am
ely
.
Utilize
th
e
d
ataset
to
tr
ain
an
d
class
if
y
b
y
im
p
lem
en
tin
g
an
in
d
u
ctiv
e
lear
n
in
g
tech
n
i
q
u
e.
Sev
er
al
al
g
o
r
ith
m
s
,
s
u
ch
as
d
ec
is
io
n
tr
e
es
an
d
n
eu
r
al
n
etwo
r
k
s
,
wer
e
in
itially
cr
ea
ted
to
d
esig
n
a
s
y
s
tem
ca
p
ab
le
o
f
ac
h
iev
in
g
t
h
is
g
o
al,
an
d
th
ei
r
in
d
iv
i
d
u
al
s
tan
d
a
r
d
p
er
f
o
r
m
an
ce
s
h
av
e
b
ee
n
th
o
r
o
u
g
h
ly
d
o
cu
m
e
n
ted
.
T
h
e
KNN
m
o
d
el
is
v
alu
ab
le
f
o
r
class
if
y
in
g
n
ewswir
e
s
to
r
ies
an
d
in
f
lu
en
cin
g
r
ea
d
er
s
b
ec
au
s
e
o
f
its
s
im
p
licity
an
d
ef
f
ec
tiv
en
ess
.
T
h
e
R
eu
ter
s
co
r
p
u
s
is
u
s
ed
to
class
if
y
co
n
tex
t,
wh
ich
h
elp
s
i
n
cr
ea
tin
g
a
KNN
m
o
d
el
t
o
im
p
r
o
v
e
ef
f
icien
c
y
with
o
u
t c
o
m
p
r
o
m
is
in
g
class
if
icatio
n
ac
cu
r
ac
y
[
2
5
]
4.
RE
SU
L
T
S AN
A
L
YS
I
S AN
D
DIS
CU
SS
I
O
N
T
h
is
ex
p
er
im
e
n
t
d
ata
s
et
co
n
t
ain
s
MRI
s
lices
f
r
o
m
B
r
aT
S1
2
to
B
r
aT
S1
8
a
n
d
3
,
0
6
4
MRI
s
lices
f
r
o
m
2
3
3
p
atien
ts
[
2
6
]
.
T
o
tal
o
f
5
0
6
3
MRI
s
am
p
les
an
d
ea
ch
o
f
th
ese
im
ag
es
m
ay
co
n
tain
s
o
n
e
o
f
th
e
th
r
ee
ty
p
es
o
f
b
r
ain
tu
m
o
r
s
:
m
en
in
g
io
m
a,
g
lio
m
a,
o
r
p
itu
itar
y
tu
m
o
r
.
T
h
e
im
ag
es
h
av
e
an
in
-
p
lan
e
r
eso
lu
tio
n
o
f
5
1
2
×
5
1
2
p
ix
els with
a
p
ix
el
s
ize
o
f
0
.
4
9
×0
.
4
9
m
m
2
.
T
h
e
th
ic
k
n
ess
o
f
th
e
s
lices
is
6
m
m
,
an
d
th
e
g
ap
b
etwe
en
th
em
is
1
m
m
.
T
h
e
im
ag
es
ar
e
p
r
ep
r
o
c
ess
ed
u
s
in
g
an
a
d
ap
tiv
e
wien
er
f
ilter
.
T
h
is
im
ag
e
p
r
ep
r
o
ce
s
s
in
g
is
r
eq
u
ir
ed
t
o
elim
in
ate
n
o
is
e
f
r
o
m
in
p
u
t
M
R
I
p
ictu
r
es,
wh
ich
im
p
r
o
v
es
t
h
e
ac
cu
r
ac
y
o
f
MRI
im
a
g
e
cla
s
s
if
icatio
n
.
Fu
zz
y
C
m
ea
n
s
m
eth
o
d
is
u
s
ed
f
o
r
im
ag
e
s
eg
m
en
tatio
n
.
T
h
e
d
ee
p
au
to
en
co
d
e
r
,
th
e
C
NN
,
an
d
th
e
KNN
alg
o
r
ith
m
s
m
ak
e
u
p
th
e
class
if
icatio
n
m
o
d
el.
T
h
e
ef
f
icac
y
o
f
th
e
u
s
ed
ap
p
r
o
ac
h
is
ass
ess
ed
u
s
in
g
m
etr
ics
lik
e
ac
cu
r
ac
y
,
s
en
s
itiv
ity
,
s
p
ec
if
icity
,
an
d
p
r
ec
is
io
n
.
Acc
u
r
ac
y
m
e
tr
ics
ar
e
in
ten
d
ed
to
ass
es
s
th
e
p
r
ec
is
io
n
o
f
a
m
eth
o
d
'
s
p
r
ed
ictio
n
s
.
T
h
e
p
r
o
p
o
r
tio
n
o
f
b
r
ain
m
alig
n
an
c
ies
ac
cu
r
ately
id
en
tifie
d
r
elativ
e
to
th
e
o
v
er
all
co
u
n
t
o
f
b
r
ai
n
tu
m
o
r
s
is
ter
m
ed
class
if
icatio
n
ac
cu
r
ac
y
.
A
s
en
s
itiv
ity
an
aly
s
is
is
p
er
f
o
r
m
ed
t
o
ev
alu
ate
th
e
in
f
lu
e
n
ce
o
f
f
lu
ctu
atin
g
v
al
u
es
an
d
p
ar
am
eter
s
o
f
a
m
o
d
el
o
n
th
e
f
in
al
r
esu
lt.
T
h
e
ca
p
ac
ity
o
f
t
h
e
d
ata
class
if
icatio
n
m
etr
ic
to
p
r
ec
is
ely
id
en
tify
ac
tu
al
p
o
s
itiv
e
v
alu
es
is
ess
en
tial.
T
h
e
s
p
ec
if
icity
o
f
a
d
at
a
ca
teg
o
r
izatio
n
m
ea
s
u
r
e
d
en
o
tes
its
ca
p
ac
ity
to
r
eliab
ly
id
en
tif
y
r
ea
l
n
eg
ativ
e
v
alu
es.
Pre
cisi
o
n
is
m
ea
s
u
r
e
d
b
y
th
e
n
u
m
b
e
r
o
f
co
r
r
ec
tly
an
ticip
ated
p
o
s
itiv
e
r
esu
lts
.
T
h
e
r
esu
lts
ar
e
s
h
o
wn
in
T
ab
le
1
a
n
d
Fig
u
r
e
4
.
T
h
e
a
cc
u
r
ac
y
o
f
th
e
d
ee
p
a
u
to
en
c
o
d
er
is
9
8
.
8
1
%
.
T
h
e
ac
cu
r
ac
y
o
f
th
e
C
NN
is
9
5
.
5
0
%,
wh
er
ea
s
th
e
ac
cu
r
ac
y
o
f
th
e
KNN
ap
p
r
o
ac
h
is
9
1
.
3
0
%.
T
ab
le
1
.
R
esu
lts
o
f
d
ee
p
a
u
to
en
co
d
er
f
o
r
b
r
ain
t
u
m
o
r
d
etec
tio
n
A
c
c
u
r
a
c
y
P
r
e
c
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o
n
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p
e
c
i
f
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c
i
t
y
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e
n
s
i
t
i
v
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t
y
K
N
N
9
1
.
3
9
2
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3
1
9
1
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9
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.
6
6
C
N
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9
5
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5
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7
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3
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4
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5
6
D
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e
p
a
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t
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o
d
e
r
9
8
.
8
1
9
7
.
5
3
9
8
.
2
8
9
6
.
3
7
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
E
n
h
a
n
ce
d
d
ee
p
a
u
to
en
c
o
d
er
tech
n
iq
u
e
fo
r
b
r
a
i
n
tu
mo
u
r
…
(
S
ye
d
Ja
h
a
n
g
ir
B
a
d
a
s
h
a
h
)
2037
Fig
u
r
e
4
.
R
esu
lts
o
f
d
ee
p
au
to
en
co
d
er
,
C
NN
an
d
KNN
tech
n
iq
u
es f
o
r
b
r
ain
t
u
m
o
r
d
etec
tio
n
5.
CO
NCLU
SI
O
N
AND
F
U
T
U
RE
WO
RK
I
n
th
is
r
esear
ch
wo
r
k
,
an
im
p
r
o
v
ed
d
e
ep
au
to
-
en
co
d
er
tech
n
iq
u
e
f
o
r
b
r
ain
tu
m
o
r
class
if
icatio
n
an
d
d
etec
tio
n
.
MRI
im
ag
es
ar
e
o
b
tain
ed
u
s
in
g
B
r
aT
S
d
atasets
.
MRI
p
ictu
r
es
m
ay
s
h
o
w
o
n
e
o
f
th
r
ee
f
o
r
m
s
o
f
b
r
ain
tu
m
o
r
s
:
m
en
in
g
io
m
a
,
g
l
io
m
a,
o
r
p
itu
itar
y
tu
m
o
r
.
T
h
e
p
h
o
to
s
ar
e
p
r
ep
r
o
ce
s
s
ed
with
an
ad
ap
tiv
e
wien
er
f
ilter
.
T
h
is
im
ag
e
p
r
ep
r
o
ce
s
s
in
g
is
r
eq
u
ir
ed
t
o
r
em
o
v
e
n
o
is
e
f
r
o
m
in
p
u
t
MRI
im
ag
es,
h
e
n
ce
im
p
r
o
v
in
g
th
e
ac
cu
r
ac
y
o
f
MRI
im
ag
e
class
if
icatio
n
.
T
h
e
f
u
zz
y
C
m
ea
n
s
ap
p
r
o
ac
h
is
u
s
ed
f
o
r
im
a
g
e
s
eg
m
en
tatio
n
.
T
h
e
class
if
icatio
n
m
o
d
el
is
m
ad
e
u
p
o
f
th
r
ee
alg
o
r
ith
m
s
:
d
ee
p
au
to
en
co
d
er
,
C
NN
,
an
d
KNN
.
Acc
u
r
ac
y
o
f
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