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re
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a
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t
h
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tal
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a
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t
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se
a
rc
h
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p
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se
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ts
th
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first
a
p
p
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c
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ti
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v
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l
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c
c
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te an
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s.
K
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d
s
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tio
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p
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r
ac
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d
is
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if
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T
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AP U
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Pra
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s
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v
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in
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I
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RO
D
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m
o
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r
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d
ev
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m
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co
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at
af
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h
y
p
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ac
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d
is
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d
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(
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,
with
a
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p
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s
f
o
r
b
o
th
p
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an
d
th
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f
am
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[
1
]
.
C
lin
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ju
d
g
m
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t
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th
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p
r
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m
ar
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m
et
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[
2
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Alth
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I
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C
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p
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g
,
Vo
l.
15
,
No
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4
,
Au
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u
s
t
20
25
:
3
9
6
5
-
3976
3966
d
is
o
r
d
er
,
ADHD
h
as
r
ec
o
g
n
i
za
b
le
p
atter
n
s
o
f
b
r
ain
ac
tiv
it
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th
at
f
ac
ilit
ate
d
iag
n
o
s
is
.
New
d
ev
elo
p
m
en
ts
in
m
ac
h
in
e
lear
n
in
g
(
ML
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a
n
d
m
ed
ical
im
ag
in
g
p
r
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t
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co
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ag
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o
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m
o
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e
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iag
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o
s
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.
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r
ain
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ata
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u
s
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d
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s
tr
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ctu
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al
m
ag
n
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eso
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ce
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ag
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g
(
s
MRI)
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d
f
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n
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al
m
ag
n
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im
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g
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f
MRI)
,
wh
ich
im
p
r
o
v
e
d
iag
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o
s
tic
ac
cu
r
ac
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[
3
]
.
Fo
r
in
s
tan
ce
,
Yilin
et
a
l
.
[
4
]
u
s
ed
g
r
ap
h
co
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v
o
lu
tio
n
al
n
et
wo
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k
s
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GC
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to
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n
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ata,
w
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ich
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p
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o
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y
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m
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s
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ased
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p
o
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etwo
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k
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wa
s
p
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l
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[
5
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u
s
in
g
r
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-
s
tate
f
MRI
d
ata
with
tim
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d
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n
ec
tiv
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en
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a
tio
n
(
AFC
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f
o
r
s
p
atial
c
o
r
r
el
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an
aly
s
is
.
T
h
r
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h
th
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in
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f
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p
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an
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ad
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s
p
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eu
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ea
r
l
ier
ap
p
r
o
ac
h
es,
o
p
en
in
g
th
e
d
o
o
r
to
m
o
r
e
p
r
ec
is
e
an
d
i
m
p
ar
tial d
iag
n
o
s
is
o
f
ADHD
.
R
ec
en
t
ad
v
an
ce
m
en
ts
in
ML
an
d
d
ee
p
lear
n
in
g
(
DL
)
h
av
e
s
ig
n
if
ican
tly
en
h
an
ce
d
th
e
d
ia
g
n
o
s
is
o
f
ADHD
an
d
o
th
er
p
s
y
c
h
iatr
ic
d
is
o
r
d
e
r
s
th
r
o
u
g
h
t
h
e
in
teg
r
ati
o
n
o
f
n
eu
r
o
im
ag
in
g
d
ata.
Fo
r
ex
am
p
le,
Hata
m
i
et
a
l.
[
6
]
s
h
o
wed
h
o
w
to
im
p
r
o
v
e
th
e
d
iag
n
o
s
is
o
f
m
ajo
r
d
ep
r
e
s
s
iv
e
d
is
o
r
d
er
b
y
u
s
in
g
f
MRI
d
ata
in
co
n
ju
n
ctio
n
with
th
e
Mo
b
ileNet
V2
m
o
d
el
an
d
th
e
d
ata
p
r
o
ce
s
s
in
g
a
n
d
an
a
ly
s
is
f
o
r
b
r
ain
im
ag
in
g
to
o
lb
o
x
.
L
iu
et
a
l.
[
7
]
d
ev
elo
p
e
d
th
e
m
u
ltimo
d
al
g
e
n
er
ativ
e
f
u
s
io
n
f
r
am
ewo
r
k
,
in
teg
r
atin
g
f
MRI
an
d
s
MRI
d
at
a
th
r
o
u
g
h
m
u
lti
-
task
lear
n
in
g
to
g
en
e
r
ate
p
air
e
d
d
a
ta,
im
p
r
o
v
in
g
d
iag
n
o
s
tic
ac
cu
r
ac
y
.
Als
h
ar
if
et
a
l.
[
8
]
d
ev
el
o
p
ed
a
n
ML
-
b
ased
d
ec
is
io
n
s
y
s
tem
th
at
ac
h
iev
ed
9
1
%
ac
cu
r
ac
y
o
n
s
tan
d
ar
d
A
DHD
d
atasets
,
lo
wer
in
g
s
u
b
jectiv
ity
in
tr
ad
itio
n
al
ev
alu
atio
n
s
;
an
d
Ag
ar
w
al
et
a
l.
[
9
]
u
s
ed
a
d
u
a
l
a
p
p
r
o
ac
h
,
co
m
b
in
in
g
im
a
g
e
-
b
ased
DL
m
o
d
els
an
d
g
r
a
p
h
-
b
ased
n
etwo
r
k
s
to
an
aly
ze
f
MRI
co
n
n
ec
tiv
ity
m
atr
ices,
s
h
o
win
g
th
at
d
if
f
er
en
t
b
r
ai
n
a
tlas
an
d
co
n
n
ec
tio
n
m
atr
ix
s
elec
tio
n
s
im
p
r
o
v
e
class
if
icatio
n
ac
cu
r
ac
y
.
I
n
co
n
tr
a
s
t
to
co
n
v
en
tio
n
al
R
eHo
tech
n
iq
u
es,
Gü
lh
an
an
d
Özm
en
[
1
0
]
s
h
o
wn
th
at
m
ain
tain
in
g
s
p
atial
in
f
o
r
m
atio
n
im
p
r
o
v
es
class
if
icati
o
n
b
y
u
s
in
g
3
D
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
(
C
NNs
)
to
ev
alu
ate
f
r
ac
tio
n
al
am
p
litu
d
e
o
f
lo
w
f
r
e
q
u
en
c
y
f
lu
ctu
ati
o
n
d
ata.
I
n
o
r
d
e
r
to
im
p
r
o
v
e
b
r
ain
ac
tiv
ity
i
n
v
esti
g
atio
n
in
ADHD
ca
s
es,
Sau
r
ab
h
a
n
d
G
u
p
ta
[
1
1
]
r
esh
ap
e
d
4
D
p
ictu
r
es
an
d
u
tili
ze
d
a
m
o
d
if
ied
b
id
ir
ec
tio
n
al
lo
n
g
s
h
o
r
t
-
ter
m
m
e
m
o
r
y
(
B
L
STM
)
m
o
d
el
to
in
ter
p
r
et
r
esti
n
g
-
s
tate
f
MRI
d
ata.
I
n
o
r
d
er
to
g
et
h
ig
h
co
m
p
u
tatio
n
al
ef
f
icien
c
y
,
Salah
et
a
l.
[
1
2
]
c
o
m
b
in
e
d
f
MRI
an
d
o
p
tical
am
p
lific
atio
n
d
ata
in
th
eir
r
esid
u
al
lear
n
in
g
lay
er
-
b
ased
ADHD
s
cr
ee
n
in
g
m
o
d
el.
I
n
a
n
ef
f
o
r
t
to
cr
ea
t
e
"e
x
p
lain
ab
le
AI
,
"
Am
ad
o
-
C
ab
aller
o
et
a
l.
[
1
3
]
ev
alu
ated
d
em
o
g
r
ap
h
ic
p
ar
a
m
eter
s
in
f
lu
en
cin
g
ADHD
d
i
ag
n
o
s
is
u
s
in
g
C
NN
v
is
u
aliza
tio
n
tech
n
iq
u
es
i
n
clu
d
in
g
o
cc
l
u
s
io
n
m
a
p
s
.
Usi
n
g
a
tr
an
s
f
o
r
m
er
-
b
ased
m
o
d
el
u
s
in
g
ADHD
-
2
0
0
f
MRI
d
ata,
Qin
et
a
l.
[
1
4
]
c
o
m
b
in
e
d
p
h
en
o
ty
p
ic
an
d
f
MRI
d
ata
to
o
b
tain
7
4
.
5
%
class
if
icatio
n
ac
cu
r
ac
y
,
s
u
r
p
ass
in
g
ea
r
lier
m
eth
o
d
s
.
T
o
f
ac
ilit
ate
ef
f
ec
tiv
e
f
ea
tu
r
e
ex
tr
ac
ti
o
n
,
a
4
D
C
NN
m
o
d
el
was
also
ap
p
l
ied
,
ca
p
tu
r
in
g
b
o
th
s
p
atial
an
d
tem
p
o
r
al
d
im
en
s
io
n
s
with
an
o
b
s
er
v
ed
ac
c
u
r
ac
y
o
f
7
1
.
3
%
[
1
5
]
.
Ar
o
u
n
d
8
0
0
p
eo
p
le
f
r
o
m
v
a
r
io
u
s
u
n
iv
er
s
ities
co
n
tr
ib
u
ted
s
MRI
an
d
r
est
in
g
-
s
tate
f
MRI
d
ata
t
o
th
e
ADHD
-
2
0
0
co
n
s
o
r
tiu
m
,
wh
ich
was
cr
ea
ted
b
y
th
e
I
NDI
in
2
0
1
1
.
T
h
is
al
lo
wed
r
esear
ch
er
s
wo
r
ld
wid
e
to
im
p
r
o
v
e
ADHD
class
if
ica
tio
n
alg
o
r
it
h
m
s
b
y
u
s
in
g
co
n
s
is
ten
t
d
atasets
[
1
6
]
.
T
h
is
in
f
o
r
m
atio
n
m
ad
e
it
ea
s
ier
to
cr
ea
te
s
o
p
h
is
ticated
m
o
d
els.
Fo
r
ex
am
p
le,
Ma
o
et
a
l.
[
1
5
]
ac
h
iev
ed
im
p
r
o
v
ed
class
if
icatio
n
ac
cu
r
ac
y
b
y
co
m
b
i
n
in
g
3
D
C
NNs
f
o
r
s
p
atial
ex
tr
ac
tio
n
with
an
lo
n
g
s
h
o
r
t
-
ter
m
m
e
m
o
r
y
(
L
STM
)
m
o
d
el
to
ca
p
tu
r
e
tem
p
o
r
al
r
elatio
n
s
h
ip
s
.
I
n
o
r
d
er
to
e
v
alu
ate
d
em
o
g
r
a
p
h
ic
asp
ec
ts
in
f
lu
e
n
cin
g
ADHD
d
iag
n
o
s
is
,
L
iu
et
al
.
[
1
7
]
in
v
esti
g
ated
C
NN
v
is
u
aliza
tio
n
ap
p
r
o
ac
h
es,
s
u
ch
as
o
cc
l
u
s
io
n
m
ap
s
.
T
h
is
wo
r
k
co
n
tr
i
b
u
t
ed
to
th
e
d
ev
elo
p
in
g
to
p
ic
o
f
"e
x
p
lain
ab
le
AI
,
"
wh
ich
im
p
r
o
v
es
th
e
in
ter
p
r
eta
b
ilit
y
o
f
DL
m
o
d
els
in
clin
ical
p
r
ac
tice.
Fin
ally
,
ef
f
o
r
ts
to
en
h
an
ce
d
iag
n
o
s
tic
ac
cu
r
ac
y
h
a
v
e
b
ee
n
ex
em
p
lifie
d
b
y
Qin
et
a
l
.
[
1
2
]
w
h
o
em
p
lo
y
ed
a
tr
an
s
f
o
r
m
er
-
b
ased
m
o
d
el
u
s
in
g
ADHD
-
2
0
0
f
MRI
im
ag
es,
co
m
b
in
in
g
f
MRI
an
d
p
h
en
o
t
y
p
ic
d
ata
to
ac
h
ie
v
e
a
class
if
icati
o
n
a
cc
u
r
ac
y
o
f
7
4
.
5
%,
s
u
r
p
ass
in
g
m
u
ltip
le
ad
v
a
n
ce
d
ap
p
r
o
ac
h
es.
T
o
f
ac
ilit
ate
ef
f
ec
tiv
e
f
ea
tu
r
e
ex
tr
ac
tio
n
,
a
4
D
C
NN
m
o
d
el
was
also
ap
p
lied
,
ca
p
t
u
r
in
g
b
o
th
s
p
atial
an
d
tem
p
o
r
al
d
im
e
n
s
io
n
s
with
an
o
b
s
er
v
e
d
ac
cu
r
ac
y
o
f
7
1
.
3
%
.
T
h
is
s
tu
d
y
im
p
r
o
v
es
th
e
d
etec
tio
n
o
f
ADHD
b
y
in
tr
o
d
u
cin
g
th
r
ee
m
ajo
r
co
n
tr
ib
u
tio
n
s
:
i
)
a
co
n
v
o
lu
ti
o
n
al
g
ated
m
em
o
r
y
u
n
it
(
GM
U)
to
ca
p
tu
r
e
s
p
ati
al
an
d
tem
p
o
r
al
in
f
o
r
m
atio
n
;
ii
)
a
m
u
lti
-
lay
er
c
o
n
v
o
lu
tio
n
al
d
en
o
is
in
g
au
to
en
co
d
er
n
etwo
r
k
to
r
ec
o
g
n
ize
3
D
s
p
atial
p
atter
n
s
in
r
es
tin
g
-
s
tate
f
u
n
c
tio
n
al
m
ag
n
etic
r
eso
n
an
ce
im
ag
in
g
(
rs
-
f
MRI
)
d
ata;
an
d
iii
)
a
v
alid
ated
f
r
am
ewo
r
k
ev
alu
ated
ac
r
o
s
s
v
ar
io
u
s
s
ites
.
T
h
e
o
v
er
v
iew
o
f
th
e
alg
o
r
ith
m
,
its
co
n
ce
p
tu
al
u
n
d
er
p
i
n
n
in
g
s
,
th
e
ex
p
er
im
en
tal
s
ettin
g
,
an
d
its
co
n
s
eq
u
en
ce
s
an
d
f
u
tu
r
e
d
ir
e
c
tio
n
s
ar
e
co
v
er
ed
i
n
th
e
p
ap
er
.
2.
AL
G
O
RI
T
H
M
T
h
e
alg
o
r
ith
m
f
o
r
class
if
y
in
g
ADHD
f
r
o
m
r
esti
n
g
-
s
tate
f
M
R
I
d
ata
ex
tr
ac
ts
s
p
atial
f
ea
tu
r
es
u
s
in
g
an
h
ier
ar
ch
ical
r
esid
u
al
c
o
n
v
o
lu
tio
n
al
n
o
is
e
r
ed
u
ctio
n
a
u
to
en
c
o
d
er
(
HR
C
NR
AE
)
,
wh
ich
is
th
en
s
tr
en
g
th
en
e
d
b
y
r
esid
u
al
co
n
n
ec
tio
n
s
to
p
r
eser
v
e
s
ig
n
if
ican
t
in
f
o
r
m
atio
n
.
Du
r
in
g
tr
ain
in
g
,
th
e
m
o
d
el
r
e
co
n
s
tr
u
cts
th
e
in
p
u
t
wh
ile
m
ea
s
u
r
in
g
r
ec
o
n
s
tr
u
ctio
n
lo
s
s
,
an
d
a
co
n
v
o
l
u
tio
n
al
GM
U
r
ec
o
r
d
s
tem
p
o
r
al
d
ep
e
n
d
en
cy
.
T
h
en
,
u
s
in
g
g
lo
b
al
a
v
er
ag
e
p
o
o
lin
g
,
p
e
o
p
l
e
ar
e
ca
teg
o
r
ize
d
as
eith
e
r
AD
HD
-
p
o
s
itiv
e
o
r
n
o
t,
o
f
f
er
in
g
a
q
u
ick
a
n
d
ef
f
ec
tiv
e
way
to
ex
am
in
e
in
tr
icate
n
eu
r
o
im
ag
in
g
d
ata.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
Mu
lti
-
la
ye
r
co
n
vo
lu
tio
n
a
l
a
u
t
o
en
co
d
er fo
r
r
ec
o
g
n
iz
in
g
th
r
ee
-
d
imen
s
io
n
a
l
…
(
Za
r
in
a
B
eg
u
m
)
3967
Alg
o
r
ith
m
1
: Sp
atio
-
tem
p
o
r
al
f
ea
tu
r
e
ex
tr
ac
tio
n
an
d
ADHD
c
lass
if
icatio
n
I
n
p
u
t
:
X
i
nput
:
4
D
r
e
s
t
i
n
g
-
st
a
t
e
f
M
R
I
(
r
s
-
f
M
R
I
)
d
a
t
a
.
S
t
e
p
1
:
D
a
t
a
R
e
g
u
l
a
r
i
z
a
t
i
o
n
A
d
d
G
a
u
s
si
a
n
n
o
i
s
e
f
o
r
r
e
g
u
l
a
r
i
z
a
t
i
o
n
:
I
n
p
u
t
X
i
npu
t
=
X
i
npu
t
+
N
no
i
s
e
S
t
e
p
2
:
F
e
a
t
u
r
e
E
n
c
o
d
i
n
g
w
i
t
h
H
R
C
N
R
A
E
1.
P
a
ss X
no
i
s
y
t
h
r
o
u
g
h
C
P
b
l
o
c
k
1
:
F
1
=
H
c
p
(X
noi
s
y
)
2.
P
r
o
c
e
ss
t
h
r
o
u
g
h
C
P
b
l
o
c
k
2
:
F
2
=
H
cp
(F
1
)
3.
P
r
o
c
e
ss
t
h
r
o
u
g
h
C
P
b
l
o
c
k
3
:
F
3
=
H
cp
(F
2
)
S
t
e
p
3
:
I
n
c
o
r
p
o
r
a
t
e
R
e
s
i
d
u
a
l
b
l
o
c
k
s
C
o
m
b
i
n
e
f
e
a
t
u
r
e
s:
F
r
e
s
i
du
a
l
_o
ut
e
r
= F
3
+ H
s
k
i
p
(F
1,
F
2
)
S
t
e
p
4
:
F
e
a
t
u
r
e
D
e
c
o
d
i
n
g
1
.
D
e
c
o
d
e
1
:
D
1
= H
ud
(F
r
e
s
i
du
a
l
_
ou
t
e
r
)
2
.
D
e
c
o
d
e
2
:
D
2
= H
ud
(D
1
)
3
.
F
i
n
a
l
o
u
t
p
u
t
:
Y
r
e
c
o
ns
t
ru
c
t
e
d
=
H
ud
(D
2
)
S
t
e
p
5
:
C
o
mp
u
t
e
L
o
ss
F
u
n
c
t
i
o
n
:
c
a
l
c
u
l
a
t
e
r
e
c
o
n
s
t
r
u
c
t
i
o
n
l
o
ss
S
t
e
p
6
:
S
p
a
t
i
o
n
-
Te
m
p
o
r
a
l
F
e
a
t
u
r
e
E
x
t
r
a
c
t
i
o
n
w
i
t
h
G
M
U
F
o
r
e
a
c
h
t
i
me
st
e
p
t
1
.
R
e
s
e
t
g
a
t
e
:
r
t
=
Ϫ(
W
r
.
h
t
-
1
+
U
r
.x
t
)
2
.
U
p
d
a
t
e
g
a
t
e
:
z
t
=
Ϫ(
W
z
.h
t
-
1
+U
z
.x
t
)
3
.
C
a
n
d
i
d
a
t
e
h
i
d
d
e
n
s
t
a
t
e
:
̃
h
t
=
t
a
n
h
(
W
h
.
(
r
t
Θ
h
t
-
1
)
+
U
h
.
x
t
)
4
.
F
i
n
a
l
h
i
d
d
e
n
st
a
t
e
:
h
t
=
(
1
-
z
t
)
Θ
h
t
-
1
+
z
t
Θ
̃
h
t
)
S
t
e
p
7
:
C
l
a
ssi
f
i
c
a
t
i
o
n
:
u
si
n
g
G
l
o
b
a
l
A
v
e
r
a
g
e
P
o
o
l
i
n
g
O
=
G
A
P
(
h
T
)
O
u
t
p
u
t
:
P
r
e
d
i
c
t
e
d
A
D
H
D
l
a
b
e
l
(
1
f
o
r
A
D
H
D
,
0
f
o
r
n
o
n
-
ADHD).
3.
P
RO
P
O
SE
D
M
E
T
H
O
D
DL
h
as
b
ee
n
u
s
ed
ex
ten
s
iv
el
y
f
o
r
a
v
ar
iety
o
f
ap
p
licatio
n
s
b
ec
au
s
e
it
is
e
f
f
ec
tiv
e
at
ex
tr
ac
tin
g
f
ea
tu
r
es
f
r
o
m
h
u
g
e
d
atasets
.
I
t
is
d
is
tin
g
u
is
h
ed
b
y
its
co
m
p
l
ex
ity
an
d
m
o
r
e
ac
cu
r
ate
p
r
ed
i
ctio
n
s
co
m
p
ar
e
d
to
co
n
v
en
tio
n
al
m
ac
h
in
e
lear
n
i
n
g
m
eth
o
d
s
.
T
h
is
r
esear
ch
p
r
esen
ts
a
n
etwo
r
k
ar
c
h
itectu
r
e
th
at
co
m
b
i
n
es
a
co
n
v
o
l
u
tio
n
al
GM
U
with
an
HR
C
NR
AE
to
u
s
e
r
s
-
f
MRI
d
ata
to
o
b
tain
jo
in
t
s
p
atio
tem
p
o
r
al
p
r
o
p
e
r
ties
,
wh
ich
ar
e
th
en
ap
p
lied
to
th
e
class
if
icatio
n
o
f
ADHD
.
As
illu
s
t
r
ated
in
Fig
u
r
e
1
,
th
e
en
co
d
er
p
ar
t
o
f
th
e
au
to
en
co
d
er
f
i
r
s
t
ex
tr
ac
ts
h
ig
h
-
le
v
el
s
p
atial
ch
ar
ac
ter
is
tics
f
r
o
m
th
e
r
s
-
f
MRI.
T
h
e
au
to
e
n
c
o
d
er
in
co
r
p
o
r
ates
a
r
esid
u
al
n
etwo
r
k
t
o
f
u
r
th
er
c
ap
tu
r
e
d
ee
p
er
s
p
atial
f
ea
t
u
r
e
s
.
T
h
e
c
o
n
v
o
lu
tio
n
al
GM
U
t
h
en
o
r
g
a
n
izes
an
d
p
r
o
ce
s
s
es
th
ese
s
p
atial
ch
ar
ac
ter
is
tics
in
a
tem
p
o
r
al
m
a
n
n
er
,
ca
p
tu
r
in
g
b
o
th
s
p
atial
an
d
tem
p
o
r
al
d
y
n
am
ics
at
th
e
s
am
e
tim
e.
T
h
e
GM
U
-
ex
tr
ac
ted
ch
ar
ac
ter
is
tics
ar
e
s
u
b
jecte
d
to
GAP
[
1
8
]
b
ef
o
r
e
b
e
in
g
s
u
b
jecte
d
to
a
s
ig
m
o
id
class
if
ier
f
o
r
th
e
u
ltima
te
ADHD
class
if
icatio
n
.
Fig
u
r
e
1
.
Flo
wch
ar
t
o
f
th
e
p
r
o
p
o
s
ed
class
i
f
icatio
n
alg
o
r
ith
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
4
,
Au
g
u
s
t
20
25
:
3
9
6
5
-
3976
3968
3
.
1
.
H
ier
a
rc
hica
l
re
s
idu
a
l c
o
nv
o
lutio
na
l no
is
e
re
du
ct
io
n
a
uto
enco
der
T
h
e
HR
C
N
R
AE
is
a
n
ew
m
eth
o
d
th
at
co
m
b
in
es
r
esid
u
al
n
etwo
r
k
s
with
co
n
v
o
lu
tio
n
al
d
en
o
is
in
g
au
to
en
co
d
er
s
(
C
DAE
)
to
ex
tr
ac
t
s
p
atial
in
f
o
r
m
atio
n
f
r
o
m
u
n
lab
eled
r
s
-
f
MRI
d
ata
[
1
9
]
.
I
ts
m
ain
g
o
al
is
to
r
ec
o
v
er
h
ig
h
-
le
v
el
s
p
atial
ch
a
r
ac
ter
is
tics
wh
ile
lo
wer
in
g
d
im
en
s
io
n
ality
s
o
th
at
th
e
c
o
n
v
o
lu
tio
n
al
GM
U
ca
n
m
o
r
e
ea
s
ily
e
x
tr
ac
t
s
p
atio
t
em
p
o
r
al
f
ea
t
u
r
es.
HR
C
NR
AE
u
s
es
s
k
ip
co
n
n
ec
tio
n
s
to
p
r
eser
v
e
h
ig
h
-
lev
el
f
ea
tu
r
e
ex
tr
ac
tio
n
wh
ile
u
s
in
g
t
h
e
r
esid
u
al
lear
n
in
g
tech
n
iq
u
e
f
ir
s
t
p
r
esen
ted
b
y
He
et
a
l.
[
2
0
]
to
a
v
o
id
th
e
v
an
is
h
in
g
g
r
ad
ien
t
is
s
u
e
in
d
ee
p
n
etw
o
r
k
s
.
T
h
e
en
c
o
d
er
,
d
ec
o
d
er
,
an
d
r
esid
u
al
b
lo
ck
s
m
a
k
e
u
p
th
e
HR
C
NR
AE
ar
ch
itectu
r
e,
as
s
ee
n
in
Fig
u
r
e
2
.
T
h
er
e
ar
e
th
r
ee
C
P
s
eg
m
en
ts
with
d
ata
f
ilter
in
g
an
d
r
ed
u
ctio
n
lay
er
s
in
th
e
in
p
u
t
p
r
o
ce
s
s
in
g
u
n
it,
an
d
th
r
ee
u
n
p
o
o
lin
g
d
ec
o
n
v
o
lu
tio
n
(
UD)
s
eg
m
en
ts
with
r
ev
er
s
e
f
ilter
in
g
an
d
d
ata
ex
p
an
s
io
n
i
n
th
e
o
u
tp
u
t
p
r
o
c
ess
in
g
u
n
it.
An
ex
te
r
n
al
b
y
p
ass
with
two
C
P
an
d
two
UD
s
eg
m
en
ts
an
d
an
in
ter
n
al
b
y
p
ass
with
o
n
e
C
P a
n
d
o
n
e
UD
s
eg
m
en
t
is
th
e
two
b
y
p
ass
ar
ch
itectu
r
es th
at
g
u
ar
an
tee
ef
f
ec
tiv
e
d
ata
f
lo
w.
T
h
e
s
y
s
tem
p
er
f
o
r
m
an
ce
is
im
p
r
o
v
ed
b
y
th
ese
b
y
p
ass
es,
wh
ich
allo
w
in
p
u
t
an
d
o
u
tp
u
t
u
n
its
to
b
e
tr
ain
ed
s
im
u
ltan
eo
u
s
ly
.
Fig
u
r
e
2
.
Or
g
an
izatio
n
o
f
HR
C
NR
AE
R
eg
ar
d
in
g
th
e
p
r
o
v
id
ed
r
s
-
f
MRI
d
ata:
=
[
1
]
(
1
∈
60
×
72
×
60
)
(
1
)
w
h
er
e
th
e
tim
e
p
e
r
io
d
,
d
en
o
te
d
b
y
p
,
v
a
r
ies d
ep
en
d
i
n
g
o
n
th
e
lo
ca
tio
n
.
T
h
e
in
p
u
t o
f
HR
C
NR
AE
is
f
ir
s
t
o
b
tain
ed
b
y
ad
d
in
g
r
an
d
o
m
n
o
is
e,
wh
ich
is
r
eg
u
lar
ized
b
y
f
o
llo
win
g
a
co
n
v
e
n
tio
n
al
n
o
r
m
al
d
is
tr
ib
u
tio
n
.
As a
r
esu
lt,
we
ca
n
g
et
=
[
1
]
,
(
∈
60
×
72
×
60
)
(
2
)
T
o
ex
tr
ac
t
th
e
e
n
h
an
ce
d
p
atter
n
h
,
th
e
in
p
u
t
p
r
o
ce
s
s
in
g
s
ec
tio
n
o
f
HR
C
NR
AE
tr
an
s
f
o
r
m
s
th
e
o
r
ig
i
n
al
d
at
a
in
to
a
co
n
d
en
s
ed
r
e
p
r
esen
tatio
n
,
wh
ich
is
ℎ
=
(
)
(
3
)
T
h
e
in
ter
im
p
atter
n
h
is
r
ec
o
n
s
tr
u
cted
in
to
y
th
r
o
u
g
h
HR
C
N
R
AE
'
s
o
u
tp
u
t p
r
o
ce
s
s
in
g
s
ec
tio
n
,
wh
ich
is
=
(
ℎ
)
(
4
)
Sin
ce
th
e
g
o
al
o
f
HR
C
NR
AE
's
tr
ain
in
g
is
t
o
r
e
d
u
ce
th
e
d
is
cr
ep
an
cy
b
etwe
en
th
e
clea
n
ed
an
d
r
e
b
u
ilt
d
ata,
th
e
lo
s
s
is
d
escr
ib
ed
as
(
5
)
.
2
lo
ss
y
x
=−
(
5
)
T
h
e
m
ax
im
u
m
v
alu
e
[
2
1
]
th
a
t
ca
n
b
e
p
r
o
d
u
ce
d
with
in
th
e
p
o
o
lin
g
win
d
o
w
is
th
e
g
o
al
o
f
th
e
m
ax
p
o
o
lin
g
win
d
o
w.
L
et
s
k,
i
∈
Q
l×
m×
n
b
e
th
e
p
r
ev
i
o
u
s
lay
er
'
s
i
-
th
f
ea
tu
r
e
m
ap
,
a
n
d
let
s
k−
1
,
i
∈
Q
l×
m×
n
r
ep
r
esen
t
t
h
e
i
-
th
f
ea
tu
r
e
m
ap
in
th
e
k
-
th
lay
e
r
.
T
h
e
i
-
th
f
ea
tu
r
e
m
ap
o
f
th
e
k
-
t
h
lay
er
'
s
co
m
p
o
n
en
ts
ca
n
b
e
c
alcu
lated
as
(6
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
Mu
lti
-
la
ye
r
co
n
vo
lu
tio
n
a
l
a
u
t
o
en
co
d
er fo
r
r
ec
o
g
n
iz
in
g
th
r
ee
-
d
imen
s
io
n
a
l
…
(
Za
r
in
a
B
eg
u
m
)
3969
,
1
,
2
,
3
=
(
,
1
,
2
,
3
)
(
6
)
T
o
p
er
f
o
r
m
d
ec
o
n
v
o
lu
tio
n
,
th
e
d
ec
o
n
v
o
lu
tio
n
k
e
r
n
el
s
lid
es
ac
r
o
s
s
th
e
f
ea
tu
r
es,
wh
er
ea
s
th
e
u
n
p
o
o
lin
g
p
r
o
ce
s
s
ze
r
o
-
f
ills
th
e
laten
t f
e
atu
r
es to
r
etu
r
n
th
e
f
ea
tu
r
e
m
a
p
to
its
o
r
ig
in
al
p
ictu
r
e
s
ize
[
2
2
]
.
T
h
r
ee
C
P b
lo
ck
s
m
ak
e
u
p
th
e
en
c
o
d
er
,
a
n
d
th
e
C
P b
lo
ck
'
s
i
-
th
o
u
tp
u
t is
F
i
= H
cp
(F
i
-
1)
i=1
,
2
,
3
,
(
7
)
w
h
er
e
s
p
ec
if
ic
co
m
p
o
s
ite
f
u
n
c
tio
n
s
ar
e
u
s
ed
,
s
u
ch
p
o
o
lin
g
,
r
ec
tifie
d
lin
ea
r
u
n
it
(
R
eL
U)
,
an
d
co
n
v
o
l
u
tio
n
,
ar
e
r
ep
r
esen
ted
b
y
HC
P
[
2
3
]
.
Fo
r
i
=
1
,
Fi
-
1
r
ep
r
esen
ts
th
e
in
p
u
t
n
o
is
e
im
ag
e
I
,
a
n
d
t
h
e
f
in
al
r
e
s
u
lt
r
esu
ltin
g
f
r
o
m
th
e
s
u
b
s
eq
u
en
t CP
b
lo
ck
f
o
r
al
l o
th
er
v
alu
es.
T
h
e
s
k
ip
-
c
o
n
n
e
ctio
n
'
s
o
u
tp
u
t is
1
=
(
∑
∗
+
)
(
8
)
T
h
e
in
ter
n
al
r
esid
u
al
b
lo
ck
i
s
r
ep
r
esen
ted
wh
en
S
I
o
r
E
=
I
an
d
i
=
2
,
an
d
th
e
o
u
te
r
r
esid
u
al
b
lo
c
k
is
r
ep
r
esen
ted
wh
er
e
i
=
1
a
n
d
S
I
o
r
E
=
E
.
T
h
e
i
-
t
h
co
n
v
o
lu
ti
o
n
k
er
n
el
is
in
d
icate
d
b
y
ω
j
.
B
j
is
a
r
ep
r
esen
tatio
n
o
f
th
e
b
ias.
T
h
e
R
eL
U
ac
tiv
ati
o
n
ad
d
s
a
tin
y
d
eg
r
ee
o
f
s
p
ar
s
ity
to
th
e
tr
ain
ed
n
etwo
r
k
wh
en
j is
th
e
n
u
m
b
er
o
f
ch
an
n
els,
wh
ich
is
s
h
o
wn
as
(
9
)
.
(
)
(
)
R
e
m
a
x
0
,
L
U
x
x
=
(
9
)
T
h
r
ee
s
y
m
m
et
r
ic
UD
b
lo
c
k
s
m
ak
e
u
p
th
e
d
ec
o
d
er
co
m
p
o
n
e
n
t.
T
h
e
r
esu
lt
o
f
th
e
f
ir
s
t
UD
b
lo
ck
is
d
is
p
lay
e
d
as
(
1
0
)
:
(
)
03
UD
G
H
F
=
(
1
0
)
T
h
e
f
o
llo
win
g
is
th
e
o
u
tco
m
e
o
f
th
e
i
-
th
UD
b
lo
ck
:
(
)
1
2
,
3
i
U
D
i
G
H
G
S
i
−
=
+
=
(
1
1
)
w
h
er
e
HUD
is
f
o
r
th
e
co
m
p
o
s
ite
o
p
er
atio
n
th
at
co
m
b
in
es
s
am
p
lin
g
,
d
ec
o
n
v
o
lu
tio
n
,
an
d
R
eL
U,
an
d
S
s
tan
d
s
f
o
r
t
h
e
o
u
tp
u
t
f
r
o
m
t
h
e
s
h
o
r
tcu
t
co
n
v
o
lu
tio
n
lay
e
r
.
I
t
s
h
o
u
ld
b
e
n
o
te
d
th
at
th
e
d
ec
o
n
v
o
l
u
tio
n
u
p
s
am
p
lin
g
b
lo
ck
s
b
elo
n
g
to
th
e
d
ec
o
d
e
r
,
wh
er
ea
s
th
e
co
n
v
o
l
u
tio
n
p
o
o
lin
g
b
lo
ck
s
in
d
icate
d
a
b
o
v
e
ar
e
p
ar
t
o
f
th
e
en
co
d
er
.
T
wo
n
ested
r
esid
u
a
l
b
lo
ck
s
in
th
e
r
esid
u
al
s
tr
u
ctu
r
e
co
n
n
ec
t
th
e
en
c
o
d
er
a
n
d
d
ec
o
d
er
.
I
n
th
e
b
eg
in
n
in
g
,
th
e
in
ter
n
al
r
esid
u
al
b
lo
ck
cr
ea
tes
a
s
k
ip
-
co
n
n
e
ctio
n
b
etwe
en
th
e
en
co
d
e
r
an
d
d
ec
o
d
er
,
wh
i
ch
is
estab
lis
h
ed
b
y
(
1
2
)
1
1
1
R
S
G
=+
(
1
2
)
T
h
e
o
u
ter
r
esid
u
al
b
lo
c
k
'
s
o
u
tp
u
t is sh
o
wn
as
(
1
3
)
:
2
EE
R
S
G
=+
(
1
3
)
3
.
2
.
G
a
t
ed
co
nv
o
lutio
na
l m
e
m
o
ry
un
it
T
h
e
GM
U
is
a
s
o
p
h
is
ticated
,
p
ar
am
eter
-
ef
f
icien
t
v
ar
ian
t
o
f
th
e
tr
ad
itio
n
al
r
ec
u
r
r
en
t
n
e
u
r
a
l
n
etwo
r
k
(
R
NN
)
[
2
4
]
th
at
s
u
cc
ess
f
u
lly
r
eso
lv
es
lo
n
g
-
s
eq
u
en
ce
d
ep
e
n
d
en
ce
an
d
g
r
ad
ie
n
t
v
an
is
h
in
g
p
r
o
b
lem
s
.
I
n
th
is
s
tu
d
y
,
s
p
atial
in
f
o
r
m
ati
o
n
m
u
s
t
b
e
in
c
o
r
p
o
r
ated
in
o
r
d
e
r
t
o
ex
tr
ac
t
s
p
atio
tem
p
o
r
al
c
h
ar
ac
ter
is
tics
f
r
o
m
r
s
-
f
MRI
d
ata
u
s
in
g
co
n
v
o
lu
tio
n
al
GM
U.
C
o
n
v
o
lu
tio
n
al
GM
U
p
r
o
ce
s
s
es
s
p
atial
an
d
tem
p
o
r
al
f
ea
tu
r
es
at
th
e
s
am
e
tim
e,
in
c
o
n
tr
ast
to
co
n
v
en
tio
n
al
GM
U
[
2
5
]
.
T
h
e
in
f
o
r
m
atio
n
f
lo
w
is
m
a
n
ag
ed
b
y
m
ea
n
s
o
f
r
eset
a
n
d
u
p
d
ate
g
ates,
wh
er
e
th
e
r
eset
g
ate
elim
in
ates
s
o
m
e
p
r
e
v
io
u
s
d
ata
an
d
th
e
u
p
d
ate
g
ate
r
e
g
u
lates
th
e
am
o
u
n
t
o
f
h
is
to
r
ical
d
ata
th
at
is
k
ep
t.
B
o
th
g
ates
to
p
r
o
ce
s
s
th
e
p
r
ec
ed
i
n
g
o
u
tp
u
t (
h
t−
1
)
at
tim
e
t
a
n
d
t
h
e
cu
r
r
e
n
t
in
p
u
t
(
x
t
)
u
s
e
a
s
ig
m
o
id
ac
tiv
atio
n
f
u
n
ct
io
n
.
T
h
e
u
p
d
ate
g
ate
(
z
t
)
r
eg
u
l
ates
th
e
am
o
u
n
t
o
f
th
e
p
r
io
r
d
ata
th
at
is
r
etain
ed
,
wh
ile
th
e
r
eset
g
ate
(
r
t
)
estab
l
is
h
es
h
o
w
m
u
c
h
o
f
it
is
c
o
n
s
id
er
ed
.
C
o
n
v
o
lu
ti
o
n
with
x
t
m
u
ltip
lies
r
t
b
y
h
t−
1
to
y
ield
th
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ca
n
d
id
ate
h
id
d
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n
s
tate
̃
h
t
.
T
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p
r
ev
io
u
s
h
id
d
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s
tate
an
d
th
e
ca
n
d
id
ate
-
h
i
d
d
en
s
tate
ar
e
co
m
b
in
ed
to
d
eter
m
in
e
th
e
f
in
al
h
id
d
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s
tate
h
t
.
(
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1
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1
[
t
a
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t
t
t
t
xr
W
h
h
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(
1
6
)
T
h
e
co
n
v
o
lu
tio
n
o
p
er
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n
is
r
ep
r
esen
ted
b
y
*
,
th
e
Had
a
m
ar
d
p
r
o
d
u
ct
b
y
⊙
,
a
n
d
th
e
s
ig
m
o
id
ac
tiv
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n
f
u
n
ctio
n
b
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σ
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T
h
e
weig
h
ts
th
at
co
r
r
esp
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d
to
r
t
, z
t
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d
̃
h
t
a
r
e
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r
, W
z
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an
d
W
.
3
.
3
.
E
x
perim
ent
a
l
s
et
up
3
.
3
.
1
.
Da
t
a
des
cr
iptio
n
T
h
e
d
ev
elo
p
e
d
m
eth
o
d
is
an
aly
ze
d
an
d
p
r
o
ce
s
s
ed
u
s
in
g
th
e
Py
th
o
n
s
im
u
latio
n
p
latf
o
r
m
.
T
h
e
p
u
b
licly
av
ailab
le
ADHD
-
2
0
0
d
atab
ase,
wh
ich
co
n
tain
s
f
MRI
im
ag
es,
is
co
llected
an
d
ex
am
in
ed
f
o
r
t
h
is
s
tu
d
y
in
o
r
d
er
to
ca
r
r
y
o
u
t
th
e
ex
p
e
r
im
en
t.
d
ataset
co
m
p
r
is
ed
o
f
im
ag
i
n
g
an
d
p
h
en
o
ty
p
ic
d
ata
(
g
en
d
er
,
ag
e,
I
Q,
h
an
d
ed
n
ess
)
f
r
o
m
9
7
3
p
e
o
p
le
s
p
r
ea
d
o
v
e
r
eig
h
t
wo
r
ld
wid
e
s
ites
.
Of
th
ese,
5
8
5
h
ad
n
o
r
m
ally
d
e
v
elo
p
in
g
ty
p
ical
d
ev
el
o
p
in
g
(
T
D)
p
r
o
f
il
es,
2
6
h
ad
an
u
n
id
en
tifie
d
d
ia
g
n
o
s
is
,
an
d
3
6
2
h
ad
ADHD
.
T
h
r
ee
s
ites
—
B
r
o
wn
,
Pit
ts
b
u
r
g
h
,
an
d
W
ash
in
g
to
n
Un
iv
er
s
ity
—
wer
e
d
is
q
u
alif
ied
b
ec
au
s
e
o
f
m
is
s
in
g
d
iag
n
o
s
tic
d
ata,
wh
er
ea
s
d
ata
f
r
o
m
f
iv
e
s
ites
—
Pek
in
g
,
Neu
r
o
im
ag
in
g
,
KKI
,
OHSU,
an
d
NYU
—
wer
e
ex
am
in
ed
.
T
h
e
d
ata
p
r
o
ce
s
s
in
g
ass
is
tan
t
f
o
r
r
esti
n
g
-
s
tate
f
MRI
(
DPA
R
SF
)
to
o
ls
,
wh
i
ch
in
clu
d
e
n
o
is
e
r
ed
u
ctio
n
,
s
p
atial
s
m
o
o
th
in
g
,
n
o
r
m
aliza
tio
n
to
MN
I
s
p
ac
e
,
s
lice
-
tim
in
g
co
r
r
ec
tio
n
,
a
n
d
h
e
ad
m
o
tio
n
co
r
r
ec
tio
n
,
wer
e
a
p
p
lied
to
p
r
e
-
p
r
o
ce
s
s
rs
-
f
MRI
d
ata.
I
m
a
g
es
co
n
ta
in
in
g
o
b
jects
an
d
p
a
r
ticip
an
ts
wh
o
m
o
v
e
d
th
eir
h
ea
d
s
ex
ce
s
s
iv
ely
wer
e
d
is
q
u
alif
ied
.
9
3
,
6
5
0
f
r
am
es
wer
e
k
ep
t
f
o
r
tr
ain
in
g
th
e
H
R
C
N
R
AE
m
o
d
el
as
s
h
o
wn
i
n
T
ab
le
1
af
ter
p
r
e
-
p
r
o
ce
s
s
in
g
.
T
ab
le
1
.
B
u
ild
in
g
d
ata
u
s
in
g
t
h
e
ADHD
-
200
d
ataset
NYU
O
H
S
U
P
e
k
i
n
g
N
i
ma
g
e
K
K
I
To
t
a
l
Tr
a
i
n
i
n
g
se
t
ADHD
1
0
5
24
61
12
20
2
2
2
C
o
n
t
r
o
l
90
33
1
0
8
16
53
3
0
0
To
t
a
l
1
9
5
57
1
6
9
28
73
5
2
2
Te
st
i
n
g
s
e
t
ADHD
29
6
24
5
3
67
C
o
n
t
r
o
l
12
28
27
14
8
89
To
t
a
l
41
34
51
19
11
1
5
6
3
.
3
.
2
.
T
ra
ini
ng
o
f
mo
dels
T
h
e
en
co
d
er
f
o
r
th
e
HR
C
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
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0
8
Mu
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3971
4.
RE
SU
L
T
S AN
D
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I
SCU
SS
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N
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ev
alu
ated
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ataset
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ch
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m
a
v
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o
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ex
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im
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in
g
.
4
.
1
.
Vis
ua
liza
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n r
esu
lt
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am
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Fig
u
r
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3
[
2
6
]
p
r
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1
6
n
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al
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ts
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r
o
m
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Fig
u
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4
em
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,
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ates n
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iatio
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Fig
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r
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3
.
W
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ir
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Fig
u
r
e
4
.
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h
e
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ap
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with
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o
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ce
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s
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in
itial C
P b
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Fig
u
r
e
5
.
T
h
e
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ir
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t f
ilter
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if
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e
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r
e
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4
.
2
.
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n
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er
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lar
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is
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Fig
u
r
e
6
,
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n
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er
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r
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im
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o
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ar
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eter
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as
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n
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tco
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eg
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lar
izatio
n
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ar
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eter
r
is
es,
ac
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r
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in
c
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s
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es
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to
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o
f
0
.
3
.
B
u
t
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en
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e
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ar
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.
3
,
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e
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u
n
d
a
n
cy
a
n
d
lo
wer
p
er
f
o
r
m
a
n
ce
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
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g
,
Vo
l.
15
,
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4
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3972
Fig
u
r
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6
.
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h
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3
.
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4
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3
.
1
.
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he
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s
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l b
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e
HR
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N
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m
o
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el
s
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o
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r
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v
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o
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ar
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to
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v
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tio
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d
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is
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au
to
e
n
co
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r
(
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DAE
)
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lar
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ely
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e
to
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in
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r
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o
f
a
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al
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lo
c
k
.
T
h
is
r
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al
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lo
ck
en
h
an
ce
s
f
ea
tu
r
e
p
r
eser
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ac
r
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s
s
lay
er
s
,
allo
win
g
th
e
m
o
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el
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atter
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th
at
ar
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m
o
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m
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lex
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o
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t
lo
s
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g
im
p
o
r
tan
t
in
f
o
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n
.
As
s
h
o
wn
in
Fig
u
r
e
7
,
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n
m
etr
ics
s
u
ch
as
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e
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ea
u
n
d
er
th
e
cu
r
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e
(
AUC)
an
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ec
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o
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atin
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c
h
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ter
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tic
(
R
OC
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r
ly
d
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o
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s
tr
ate
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f
f
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im
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n
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Fig
u
r
e
7
.
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h
e
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n
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ast b
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an
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4
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3
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2
.
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s
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s
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a
n
d
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e
b
l
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f
o
r
co
n
tr
ast
in
o
r
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er
to
g
e
t
th
e
id
ea
l
n
u
m
b
er
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f
C
P
b
lo
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s
.
T
a
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le
3
d
is
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lay
s
th
e
f
in
d
in
g
s
.
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h
e
class
if
icatio
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ass
ess
m
en
t
in
d
ices'
ac
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,
s
en
s
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s
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d
ar
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am
p
les
o
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s
ed
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alu
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n
.
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h
e
p
r
o
b
ab
ilit
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th
at
th
e
m
o
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el
wo
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ld
ac
cu
r
ately
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teg
o
r
ize
T
D
an
d
ADHD
is
k
n
o
wn
as a
cc
u
r
ac
y
,
an
d
it is
d
escr
ib
ed
as
(
1
7
)
Ac
cu
r
ac
y
=
T
P +
T
N/T
P+T
N
+FP+FN
(
1
7
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
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3973
T
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r
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h
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ess
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ts
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m
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ter
v
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ab
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3
illu
s
tr
ates
h
o
w
ad
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itio
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P
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s
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n
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ea
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r
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ield
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g
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o
m
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test
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u
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e
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ates
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4
.
1
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Co
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t
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T
M
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M
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Usi
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e
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atasets
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icac
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o
f
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v
o
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U
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d
co
n
v
o
lu
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STM
was
ev
alu
ated
.
T
ab
le
4
d
em
o
n
s
tr
a
tes
th
at
co
n
v
o
lu
tio
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U
s
u
r
p
ass
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n
v
o
lu
tio
n
al
L
STM
in
class
if
icatio
n
ac
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r
ac
y
a
n
d
s
p
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icity
,
d
esp
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av
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a
m
ar
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in
ally
lo
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en
s
itiv
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.
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r
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all
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m
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ce
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ly
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m
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lex
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lar
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er
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m
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o
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M
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en
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T
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o
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s
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p
ar
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M
e
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y
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C
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4
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2
G
M
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M
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c
o
m
pa
riso
n
T
h
e
ef
f
ec
tiv
e
n
ess
o
f
th
e
c
o
n
v
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lu
ti
o
n
o
p
er
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n
is
ass
ess
ed
b
y
co
m
p
ar
in
g
th
e
ef
f
icien
cy
o
f
co
n
v
o
l
u
tio
n
al
GM
U
with
s
tan
d
ar
d
GM
U
in
o
r
d
er
to
class
if
y
ADHD
.
T
h
e
s
am
e
test
in
g
s
et
is
u
s
ed
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alu
ate
b
o
th
m
o
d
els,
an
d
t
h
e
s
am
e
tr
ai
n
in
g
s
et
is
u
s
ed
to
tr
ain
t
h
em
.
T
ab
le
5
s
h
o
ws th
e
ex
p
er
im
en
t'
s
r
esu
lts
.
T
ab
le
5
.
C
o
m
p
a
r
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o
n
o
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c
o
n
v
o
lu
tio
n
al
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U
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d
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o
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o
l
u
tio
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al
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U
M
e
t
h
o
d
o
l
o
g
y
A
c
c
u
r
a
c
y
S
e
n
s
i
t
i
v
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t
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S
p
e
c
i
f
i
c
i
t
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U
6
9
.
8
7
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6
.
2
6
%
7
0
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8
1
%
C
o
n
v
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l
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t
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l
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U
7
3
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2
2
%
7
0
.
1
4
%
7
3
.
1
4
%
4
.
5
.
Co
m
pa
riso
n wit
h o
t
her
m
et
ho
ds
T
h
e
s
u
g
g
ested
alg
o
r
ith
m
f
o
r
class
if
y
in
g
ADHD
was
co
n
tr
asted
with
f
iv
e
cu
ttin
g
-
e
d
g
e
t
ec
h
n
iq
u
es:
C
DAE
-
Ad
aDT
[
2
3
]
,
MK
L
[
2
7
]
,
MD
S
-
SVM
[
2
8
]
,
3
D
-
C
NN
[
1
9
]
,
a
n
d
4
D
-
C
NN
[
2
2
]
.
I
n
a
cc
u
r
ac
y
,
s
p
ec
if
icity
,
an
d
s
en
s
itiv
ity
,
it
o
u
tp
er
f
o
r
m
e
d
MD
S
-
SVM,
3
D
-
C
N
N,
MK
L
,
an
d
4
D
-
C
NN,
as
s
ee
n
in
T
ab
le
6
.
Ho
wev
er
,
in
s
p
ec
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icity
,
it
p
er
f
o
r
m
e
d
s
o
m
ewh
at
wo
r
s
e
th
an
C
DAE
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Ad
aDT
,
m
o
s
t
lik
ely
as
a
r
esu
lt
o
f
v
ar
iatio
n
s
in
g
en
er
aliza
tio
n
ac
r
o
s
s
d
if
f
er
en
t
s
ites
.
Fig
u
r
e
8
s
h
o
ws a
g
r
ap
h
ic
co
m
p
ar
is
o
n
o
f
th
ese
f
i
n
d
in
g
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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Vo
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15
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4
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Au
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25
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3
9
6
5
-
3976
3974
T
ab
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6
.
R
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ataset
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Fig
u
r
e
8
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C
o
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p
a
r
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g
t
h
e
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ts
o
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et
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s
4.
CO
NCLU
SI
O
N
T
h
e
f
o
u
r
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d
im
en
s
io
n
al
d
ata
r
ec
o
r
d
ed
u
s
in
g
f
u
n
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n
al
m
ag
n
etic
r
eso
n
an
ce
im
ag
i
n
g
i
n
th
e
r
e
s
tin
g
s
tate
(rs
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f
MRI)
in
co
r
p
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r
ates
o
n
e
-
d
im
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etails.
T
r
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eth
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ats
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ay
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tial
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i
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e
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n
o
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class
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n
m
eth
o
d
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ess
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h
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at
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4
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o
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f
MRI
im
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h
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o
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g
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th
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s
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atial
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y
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ata,
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u
r
ap
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r
o
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im
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e
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o
d
el'
s
ca
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r
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ely
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etec
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.
T
h
e
r
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lts
o
f
th
e
ex
p
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im
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ts
s
h
o
w
th
at
th
e
s
u
g
g
ested
m
eth
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d
f
u
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s
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ite
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,
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r
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tly
in
cr
ea
s
in
g
th
e
class
if
icatio
n
ac
cu
r
ac
y
o
f
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HD.
W
h
ile
th
is
s
tu
d
y
f
o
cu
s
es
o
n
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,
f
u
tu
r
e
r
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ch
will
aim
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b
r
o
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en
th
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ap
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o
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eth
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o
lo
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y
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o
t
h
e
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