I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
pu
t
er
E
ng
ineering
(
I
J
E
CE
)
Vo
l.
15
,
No
.
3
,
J
u
n
e
20
25
,
p
p
.
3519
~
3
5
2
6
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijece.
v
15
i
3
.
pp
3
5
1
9
-
3
5
2
6
3519
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m
A
no
v
el
co
nv
o
luti
o
na
l neural net
wo
rk
a
rchit
ectur
e f
o
r
Alzheimer
’s
disea
se cla
ss
ificatio
n u
sing
ma
g
netic
res
o
na
nce
ima
g
ing
da
ta
Su
ha
ila
Abuo
wa
ida
1
,
Z
a
i
d M
us
t
a
f
a
2
,
Ahm
a
d Aburo
m
ma
n
3
,
Na
wa
f
Als
hd
a
if
a
t
4
,
M
us
a
b Iq
t
a
it
3
1
D
e
p
a
r
t
me
n
t
o
f
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
,
F
a
c
u
l
t
y
o
f
I
n
f
o
r
ma
t
i
o
n
Te
c
h
n
o
l
o
g
y
,
A
l
a
l
-
B
a
y
t
U
n
i
v
e
r
si
t
y
,
M
a
f
r
a
q
,
J
o
r
d
a
n
2
P
r
i
n
c
e
A
b
d
u
l
l
a
h
b
i
n
G
h
a
z
i
,
F
a
c
u
l
t
y
o
f
I
n
f
o
r
mat
i
o
n
a
n
d
C
o
m
mu
n
i
c
a
t
i
o
n
Te
c
h
n
o
l
o
g
y
,
A
l
-
B
a
l
q
a
A
p
p
l
i
e
d
U
n
i
v
e
r
si
t
y
,
A
l
-
S
a
l
t
,
J
o
r
d
a
n
3
D
e
p
a
r
t
me
n
t
o
f
D
a
t
a
S
c
i
e
n
c
e
a
n
d
A
I
,
F
a
c
u
l
t
y
o
f
I
n
f
o
r
ma
t
i
o
n
T
e
c
h
n
o
l
o
g
y
,
Z
a
r
q
a
U
n
i
v
e
r
si
t
y
,
Za
r
q
a
,
J
o
r
d
a
n
4
F
a
c
u
l
t
y
o
f
I
n
f
o
r
m
a
t
i
o
n
T
e
c
h
n
o
l
o
g
y
,
A
p
p
l
i
e
d
S
c
i
e
n
c
e
P
r
i
v
a
t
e
U
n
i
v
e
r
si
t
y
,
A
mm
a
n
,
J
o
r
d
a
n
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Au
g
1
4
,
2
0
2
4
R
ev
is
ed
Feb
1
0
,
2
0
2
5
Acc
ep
ted
Ma
r
4
,
2
0
2
5
Ac
c
u
ra
te
c
a
teg
o
riza
ti
o
n
o
f
Alz
h
e
ime
r’s
d
ise
a
se
is
c
ru
c
ial
fo
r
m
e
d
ica
l
d
iag
n
o
sis
a
n
d
th
e
d
e
v
e
lo
p
m
e
n
t
o
f
th
e
ra
p
e
u
ti
c
stra
teg
ies
.
De
e
p
lea
rn
in
g
m
o
d
e
ls
h
a
v
e
sh
o
wn
si
g
n
ifi
c
a
n
t
p
o
ten
ti
a
l
in
th
is
e
n
d
e
a
v
o
r;
h
o
w
e
v
e
r,
th
e
y
o
ften
e
n
c
o
u
n
ter
d
iffi
c
u
l
ti
e
s
d
u
e
t
o
th
e
in
tri
c
a
te
a
n
d
v
a
ried
c
h
a
ra
c
teristics
o
f
Alz
h
e
ime
r’s
d
ise
a
se
.
To
a
d
d
re
s
s
th
is
d
iffi
c
u
l
ty
,
we
su
g
g
e
st
a
n
e
w
a
n
d
in
n
o
v
a
ti
v
e
a
rc
h
it
e
c
tu
re
f
o
r
Alz
h
e
i
m
e
r’s
d
ise
a
se
c
las
sific
a
ti
o
n
u
si
n
g
m
a
g
n
e
ti
c
re
so
n
a
n
c
e
d
a
ta.
Th
is
d
e
sig
n
is
n
a
m
e
d
Re
s
-
BRNe
t
a
n
d
c
o
m
b
in
e
s
d
e
e
p
re
sid
u
a
l
a
n
d
b
o
u
n
d
a
ry
-
b
a
se
d
c
o
n
v
o
lu
ti
o
n
a
l
n
e
u
ra
l
n
e
two
rk
s
(CNN
s).
Re
s
-
BRNe
t
u
ti
li
z
e
s
a
m
e
th
o
d
ica
l
fu
sio
n
o
f
b
o
u
n
d
a
r
y
-
fo
c
u
se
d
p
ro
c
e
d
u
re
s
with
in
a
d
a
p
ted
sp
a
ti
a
l
a
n
d
re
si
d
u
a
l
b
l
o
c
k
s.
T
h
e
sp
a
ti
a
l
b
lo
c
k
s
re
tri
e
v
e
in
fo
rm
a
ti
o
n
re
latin
g
t
o
u
n
ifo
rm
it
y
,
d
iv
e
rsity
,
a
n
d
b
o
u
n
d
a
ries
o
f
A
lzh
e
ime
r’s
d
ise
a
se
,
a
lt
h
o
u
g
h
t
h
e
re
sid
u
a
l
b
l
o
c
k
s
su
c
c
e
ss
fu
ll
y
c
a
p
t
u
re
tex
tu
re
d
iffere
n
c
e
s
a
t
b
o
t
h
l
o
c
a
l
a
n
d
g
lo
b
a
l
lev
e
ls.
We
c
o
n
d
u
c
ted
a
p
e
rf
o
rm
a
n
c
e
a
ss
e
ss
m
e
n
t
o
f
Re
s
-
BRNe
t
.
Th
e
Re
s
-
BRNe
t
su
rp
a
ss
e
d
c
o
n
v
e
n
ti
o
n
a
l
CNN
m
o
d
e
ls,
wit
h
o
u
tstan
d
in
g
le
v
e
ls
o
f
a
c
c
u
ra
c
y
(
9
9
.
2
2
%
).
Th
e
fi
n
d
i
n
g
s
in
d
ica
te
th
a
t
Re
s
-
BRNe
t
h
a
s
p
ro
m
ise
a
s
a
to
o
l
fo
r
c
las
sify
in
g
Alz
h
e
ime
r’s
d
ise
a
se
,
with
th
e
a
b
il
it
y
to
e
n
h
a
n
c
e
th
e
p
re
c
isio
n
a
n
d
e
ffe
c
ti
v
e
n
e
ss
o
f
c
li
n
ica
l
d
ia
g
n
o
sis
a
n
d
trea
tme
n
t
p
lan
n
in
g
K
ey
w
o
r
d
s
:
Alzh
eim
er
’
s
C
las
s
if
icatio
n
C
o
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
Dee
p
lear
n
in
g
Hea
lth
ca
r
e
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Su
h
aila
Ab
u
o
waid
a
Dep
ar
tm
en
t o
f
C
o
m
p
u
ter
Scie
n
ce
,
Facu
lty
o
f
I
n
f
o
r
m
atio
n
T
ec
h
n
o
lo
g
y
,
Al
Al
-
B
ay
t U
n
iv
er
s
ity
Ma
f
r
aq
,
J
o
r
d
an
E
m
ail: su
h
ilao
wid
a@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
Alzh
eim
er
’
s
d
is
ea
s
e
(
AD)
is
an
ev
er
-
in
c
r
ea
s
in
g
th
r
ea
t
i
n
th
e
wo
r
ld
,
p
r
o
v
in
g
th
e
n
ee
d
to
d
iag
n
o
s
e
it
an
d
d
if
f
e
r
en
tiate
it
f
r
o
m
o
th
e
r
s
im
ilar
co
n
d
itio
n
s
.
T
h
e
p
r
esen
t
th
er
ap
eu
tic
d
ia
g
n
o
s
is
d
ep
en
d
s
o
n
th
e
e
x
p
er
t
r
ea
d
in
g
o
f
r
ad
i
o
lo
g
is
ts
,
wh
ic
h
is
co
n
s
id
er
ed
e
r
r
o
n
eo
u
s
b
e
ca
u
s
e
th
ey
ar
e
h
u
m
a
n
b
ei
n
g
s
.
T
h
er
e
is
g
r
o
win
g
ev
id
en
ce
o
n
th
e
ap
p
licatio
n
o
f
d
ee
p
lear
n
i
n
g
in
ea
r
ly
AD
d
iag
n
o
s
is
[
1
]
.
AD,
a
n
eu
r
o
lo
g
ical
illn
ess
,
h
as
b
ec
o
m
e
m
o
r
e
co
m
m
o
n
a
n
d
is
to
d
ay
th
e
f
o
u
r
th
lead
i
n
g
ca
u
s
e
o
f
m
o
r
tality
in
in
d
u
s
tr
ializ
ed
n
atio
n
s
[
2
]
,
[
3
]
.
T
h
e
p
r
im
a
r
y
s
y
m
p
to
m
s
o
f
Al
zh
eim
er
’
s
d
is
ea
s
e
ar
e
m
e
m
o
r
y
lo
s
s
an
d
co
g
n
itiv
e
d
ec
lin
e,
wh
ich
o
cc
u
r
d
u
e
to
th
e
d
eg
e
n
er
atio
n
an
d
d
ea
th
o
f
n
eu
r
o
n
s
ass
o
ciate
d
with
m
em
o
r
y
in
th
e
b
r
ain
.
Mild
c
o
g
n
itiv
e
im
p
air
m
en
t
(
MCI)
is
a
s
tate
th
at
lies
b
e
twee
n
n
o
r
m
al
b
r
ain
f
u
n
ctio
n
an
d
AD
[
4
]
.
Ov
er
tim
e
,
Alzh
eim
er
’
s
d
is
ea
s
e
p
r
o
g
r
ess
es
f
r
o
m
th
e
f
ir
s
t
s
tag
e
o
f
m
o
d
er
ate
co
g
n
itiv
e
im
p
a
ir
m
en
t
(
MCI)
to
d
em
e
n
tia.
Stu
d
ies
in
d
icate
th
a
t
in
d
iv
id
u
als with
MCI
h
av
e
a
1
0
%
to
1
5
% a
n
n
u
al
r
is
k
o
f
d
ev
elo
p
in
g
Alzh
eim
er
’
s
d
is
ea
s
e
[
2
]
.
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
.
3
,
J
u
n
e
20
25
:
3
5
1
9
-
3
5
2
6
3520
E
ar
ly
id
en
tific
atio
n
o
f
p
er
s
o
n
s
with
MCI
m
ay
h
in
d
er
o
r
d
ela
y
th
e
p
r
o
g
r
ess
io
n
f
r
o
m
t
h
e
MCI
s
tag
e
to
Alzh
eim
er
’
s
d
is
ea
s
e
.
Patien
ts
in
th
e
in
ter
m
ed
iate
p
h
ases
o
f
MCI
ex
h
ib
it
s
u
b
tle
alter
atio
n
s
in
th
e
co
n
f
ig
u
r
atio
n
o
f
th
eir
ce
r
eb
r
al
lesi
o
n
s
[
5
]
.
T
h
e
m
o
s
t
r
ec
e
n
t
r
esear
ch
s
u
g
g
ests
th
at
ea
r
ly
m
o
d
er
ate
c
o
g
n
itiv
e
im
p
air
m
e
n
t
(
E
MCI)
is
s
ee
n
in
th
e
ea
r
ly
s
tag
es
o
f
MCI.
C
o
n
v
er
s
ely
,
late
m
ild
co
g
n
itiv
e
im
p
a
ir
m
en
t
(
L
MCI)
o
r
p
r
o
g
r
ess
iv
e
m
ild
co
g
n
itiv
e
im
p
air
m
en
t
(
PMC
I
)
is
ch
ar
ac
ter
ized
b
y
a
s
tead
y
wo
r
s
en
in
g
o
f
s
y
m
p
to
m
s
o
v
er
tim
e
[
6
]
.
As
s
y
m
p
t
o
m
s
ad
v
a
n
ce
an
d
tr
an
s
itio
n
f
r
o
m
o
n
e
s
tag
e
to
th
e
n
e
x
t,
m
ed
ical
p
r
o
f
ess
io
n
als
ex
er
cise
m
o
r
e
ca
u
tio
n
.
R
esear
ch
er
s
m
a
y
f
ac
e
ch
allen
g
es
wh
en
tr
y
in
g
to
d
is
ce
r
n
v
ar
iatio
n
s
in
s
p
ec
i
f
ic
s
y
m
p
to
m
s
ac
r
o
s
s
d
if
f
er
en
t
p
o
p
u
latio
n
s
.
Po
s
itro
n
em
is
s
io
n
to
m
o
g
r
ap
h
y
(
P
E
T
)
,
m
ag
n
etic
r
eso
n
an
ce
im
ag
in
g
(
MRI)
,
an
d
co
m
p
u
ted
to
m
o
g
r
ap
h
y
(
C
T
)
ar
e
d
is
tin
ct
m
ed
ical
im
ag
in
g
m
eth
o
d
s
th
at
p
r
o
v
id
e
s
tan
d
ar
d
iz
ed
test
in
g
f
o
r
m
ats
an
d
ess
en
tial im
ag
es f
o
r
ex
p
er
im
en
tal
o
p
er
atio
n
s
in
th
e
ar
ea
o
f
m
ed
icin
e
[
7
]
.
M
R
I
is
wi
d
e
l
y
r
e
c
o
g
n
i
z
e
d
as
a
v
e
r
y
e
f
f
i
c
i
e
n
t
a
n
d
s
e
c
u
r
e
t
ec
h
n
o
l
o
g
y
f
o
r
d
e
t
e
c
t
i
n
g
m
a
n
y
c
o
n
d
i
t
i
o
n
s
,
i
n
c
l
u
d
i
n
g
b
r
a
i
n
t
u
m
o
r
s
,
n
e
u
r
o
l
o
g
i
c
a
l
il
l
n
es
s
es
,
s
p
i
n
a
l
c
o
r
d
i
n
j
u
r
i
e
s
a
n
d
a
b
n
o
r
m
a
l
it
i
es
,
a
n
d
l
i
v
e
r
d
i
s
e
as
es
[
8
]
–
[
1
0
]
.
T
h
e
e
x
c
e
p
t
i
o
n
a
l
s
e
n
s
i
t
i
v
i
t
y
o
f
t
h
is
t
e
c
h
n
i
q
u
e
e
n
a
b
l
es
e
a
r
l
y
d
e
t
e
c
ti
o
n
o
f
d
i
s
e
a
s
es
,
h
e
n
c
e
e
n
h
a
n
c
i
n
g
i
t
s
v
e
r
s
a
t
il
i
t
y
[
1
1
]
–
[
1
4
]
.
T
h
e
d
i
s
t
i
n
c
t
a
t
t
r
i
b
u
t
es
o
f
v
a
r
i
o
u
s
M
R
I
s
e
q
u
e
n
c
e
s
m
a
y
b
e
a
d
v
a
n
t
a
g
e
o
u
s
f
o
r
a
r
a
n
g
e
o
f
m
e
d
i
c
a
l
c
o
n
d
i
ti
o
n
s
.
T
h
is
s
t
u
d
y
p
r
e
s
e
n
t
s
a
n
o
v
e
l
a
p
p
r
o
a
c
h
f
o
r
c
l
a
s
s
i
f
y
i
n
g
Al
z
h
e
i
m
e
r
’
s
d
is
e
a
s
e
i
n
MR
I
i
m
a
g
es
u
s
i
n
g
a
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
(
C
NN
)
.
T
h
e
R
es
-
B
R
N
e
t
,
a
n
o
v
e
l
C
N
N
a
r
c
h
i
t
e
ct
u
r
e
,
i
s
p
r
o
p
o
s
e
d
as
a
s
u
i
t
a
b
le
c
h
o
i
c
e
f
o
r
cl
a
s
s
i
f
y
i
n
g
A
l
z
h
e
i
m
e
r
’
s
d
is
e
as
e
.
W
e
e
v
a
l
u
a
t
e
d
t
h
e
p
r
e
d
ic
t
i
v
e
p
e
r
f
o
r
m
a
n
c
e
o
f
t
h
e
n
o
v
e
l
s
t
r
a
t
e
g
y
o
n
t
h
e
e
x
p
e
r
i
m
e
n
t
a
l
d
at
a
s
et
a
n
d
c
o
m
p
a
r
e
d
i
t
t
o
o
t
h
e
r
s
t
a
t
e
-
of
-
t
h
e
-
a
r
t
C
NN
s
a
n
d
t
r
a
d
i
ti
o
n
a
l
a
p
p
r
o
a
c
h
e
s
.
C
o
n
tr
ib
u
tio
n
s
to
th
e
p
lan
n
e
d
wo
r
k
:
T
h
e
R
es
-
B
R
Net
is
an
in
n
o
v
ativ
e
ar
c
h
itectu
r
e
th
at
c
o
m
b
in
es
d
ee
p
r
esid
u
al
an
d
r
e
g
io
n
al
co
n
v
o
l
u
tio
n
al
n
eu
r
al
n
etwo
r
k
s
.
I
t
is
s
p
ec
if
ically
b
u
ilt
to
class
if
y
Alzh
eim
er
’
s
d
is
ea
s
e.
T
h
e
R
es
-
B
R
Net
em
p
lo
y
s
a
f
u
s
io
n
o
f
s
p
atial
an
d
r
esid
u
al
b
lo
ck
s
to
ac
c
u
r
ately
ca
p
tu
r
e
co
m
p
lex
p
atter
n
s
lin
k
e
d
to
Alzh
eim
er
’
s
d
is
ea
s
e
in
b
r
ain
MRIs.
T
h
is
s
tr
ateg
y
im
p
r
o
v
es
th
e
p
r
ec
is
io
n
o
f
th
e
class
if
icatio
n
m
o
d
el
f
o
r
Alzh
eim
er
’
s
d
is
ea
s
e.
R
e
s
-
B
R
Net
u
s
ed
s
p
atial
co
n
n
ec
tio
n
s
an
d
tex
tu
r
al
v
ar
iatio
n
s
d
er
i
v
e
d
f
r
o
m
MRI
im
ag
es
o
f
p
atien
ts
d
iag
n
o
s
ed
with
Alzh
eim
er
’
s
d
is
ea
s
e.
T
h
e
u
s
e
o
f
r
eg
io
n
al
an
d
b
o
u
n
d
ar
y
-
b
as
ed
tech
n
iq
u
es
in
a
p
r
ed
eter
m
in
e
d
o
r
d
e
r
in
s
id
e
d
if
f
er
en
t
s
p
atial
an
d
r
esid
u
a
l
b
lo
ck
s
m
ad
e
th
is
p
o
s
s
ib
le
.
T
h
e
R
es
-
B
R
Net
co
n
v
o
l
u
tio
n
al
n
e
u
r
al
n
etwo
r
k
in
co
r
p
o
r
ates
r
esid
u
al
an
d
s
p
atial
b
lo
ck
s
to
en
h
a
n
ce
d
if
f
er
e
n
tiatio
n
a
n
d
g
en
er
aliza
tio
n
.
T
h
e
ab
ilit
y
to
ex
tr
ac
t
b
o
th
b
o
u
n
d
ar
y
-
d
r
iv
e
n
p
r
o
p
er
ties
an
d
ab
s
tr
ac
t
p
att
er
n
s
is
a
f
ea
tu
r
e
o
f
s
p
atial
b
lo
ck
s
.
T
h
e
r
e
m
ain
in
g
b
lo
ck
s
at
th
e
tar
g
et
lev
el
also
g
et
in
f
o
r
m
atio
n
o
n
b
o
th
lo
ca
l
an
d
g
lo
b
al
ch
an
g
es
in
th
e
tex
tu
r
e
o
f
Alzh
eim
e
r
’
s
d
is
ea
s
e.
T
h
e
s
ec
tio
n
s
ar
e
ar
r
an
g
ed
in
th
e
f
o
llo
win
g
o
r
d
e
r
:
T
h
e
“r
elate
d
wo
r
k
”
s
ec
tio
n
p
r
esen
ts
th
e
latest
s
tu
d
ies
o
n
th
e
ea
r
ly
id
en
tific
atio
n
o
f
Alzh
eim
er
’
s
d
is
ea
s
e.
T
h
e
“m
eth
o
d
”
s
ec
tio
n
p
r
o
v
id
es
a
d
etailed
ex
p
lan
atio
n
o
f
th
e
r
ec
o
m
m
en
d
ed
ap
p
r
o
ac
h
f
o
r
d
iag
n
o
s
in
g
AD.
T
h
e
“d
is
cu
s
s
io
n
”
s
ec
tio
n
th
o
r
o
u
g
h
ly
ex
a
m
in
es
th
e
ex
p
er
im
e
n
tal
r
esu
lts
,
in
clu
d
in
g
ex
te
n
s
iv
e
ex
p
lan
atio
n
s
an
d
co
m
p
a
r
is
o
n
s
to
p
r
e
v
io
u
s
s
tu
d
ies.
T
h
e
“c
o
n
clu
s
io
n
”
s
ec
tio
n
o
f
f
er
s
a
s
u
cc
in
ct
o
v
er
v
iew
o
f
o
u
r
f
in
al
d
is
co
v
er
ies
2.
RE
L
AT
E
D
W
O
R
K
L
ately
,
th
er
e
h
as
b
ee
n
a
n
o
tab
le
s
u
r
g
e
in
th
e
u
s
e
o
f
d
ee
p
lear
n
in
g
tec
h
n
iq
u
es
to
ca
teg
o
r
ize
Alzh
eim
er
’
s
d
is
ea
s
e
b
y
ex
am
i
n
in
g
d
ata
f
r
o
m
s
ev
er
al
b
r
ain
i
m
ag
in
g
m
o
d
alities
.
Mu
ltip
le
s
tu
d
ies
h
av
e
s
h
o
wn
th
at
C
NNs
m
ay
b
e
en
h
a
n
ce
d
to
class
if
y
Alzh
eim
er
’
s
d
is
ea
s
e
b
y
u
s
in
g
s
am
p
le
d
ata
o
b
tai
n
ed
f
r
o
m
d
if
f
er
e
n
t
im
ag
in
g
m
o
d
alities
.
T
h
e
r
esea
r
ch
er
s
in
r
ef
e
r
en
ce
[
1
5
]
d
ev
el
o
p
ed
a
tech
n
iq
u
e
f
o
r
p
r
ed
ictin
g
th
e
co
n
v
er
s
io
n
o
f
MCI
u
s
in
g
d
o
m
ai
n
tr
an
s
f
er
le
ar
n
in
g
.
T
h
e
r
esear
ch
er
s
u
s
ed
s
ev
er
al
tech
n
iq
u
es,
i
n
clu
d
in
g
tar
g
et
an
d
au
x
iliar
y
d
o
m
ain
d
ata
s
am
p
les.
T
h
ey
attain
ed
a
p
r
ed
ictio
n
ac
cu
r
ac
y
o
f
7
9
.
4
0
% b
y
u
s
in
g
e
x
p
er
im
e
n
tal
m
eth
o
d
o
lo
g
ies i
n
d
o
m
ain
tr
an
s
f
er
lear
n
in
g
.
T
h
e
au
th
o
r
o
f
th
e
p
a
p
er
[
1
6
]
p
r
o
p
o
s
es
a
r
o
b
u
s
t
an
d
ef
f
icien
t
d
ee
p
-
lear
n
in
g
m
eth
o
d
th
at
lev
er
ag
es
MRI
an
d
PET
m
o
d
alities
.
T
o
en
h
an
ce
t
h
eir
ab
ilit
y
to
ca
teg
o
r
ize,
th
ey
u
s
ed
a
d
r
o
p
o
u
t
m
eth
o
d
o
l
o
g
y
.
I
n
ad
d
itio
n
,
r
esear
ch
er
s
u
s
ed
th
e
m
u
ltit
ask
lear
n
in
g
m
eth
o
d
o
lo
g
y
o
f
th
e
d
ee
p
lear
n
in
g
f
r
am
ewo
r
k
to
ass
ess
th
e
d
is
p
ar
ities
b
etwe
en
m
o
d
els
with
an
d
with
o
u
t
d
r
o
p
o
u
ts
.
T
h
e
d
r
o
p
o
u
t
m
eth
o
d
y
ield
e
d
ex
p
er
im
en
tal
f
i
n
d
in
g
s
th
at
d
e
m
o
n
s
tr
ated
a
5
.
9
% im
p
r
o
v
em
e
n
t.
A
d
if
f
er
en
t
r
esear
ch
s
tu
d
y
[
1
7
]
u
s
ed
a
Seg
Net
-
b
ased
d
ee
p
le
ar
n
in
g
a
p
p
r
o
ac
h
t
o
id
e
n
tify
th
e
s
p
ec
if
ic
lo
ca
l
b
r
ain
m
o
r
p
h
o
lo
g
ical
tr
aits
th
at
ar
e
es
s
en
tial
f
o
r
id
en
tify
in
g
Alzh
eim
er
’
s
d
is
ea
s
e.
T
h
e
r
esear
ch
er
s
f
o
u
n
d
th
at
u
s
in
g
a
d
ee
p
lear
n
in
g
ap
p
r
o
ac
h
with
a
p
r
e
-
tr
ain
e
d
m
o
d
el
s
ig
n
if
ican
tly
e
n
h
an
ce
d
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
class
if
ier
.
Me
an
wh
ile,
Ç
eleb
i
an
d
E
m
ir
o
ğ
l
u
[
1
8
]
u
s
ed
m
o
r
p
h
o
m
et
r
ic
im
ag
es
ac
q
u
ir
ed
u
s
in
g
ten
s
o
r
-
b
ased
m
o
r
p
h
o
m
etr
y
(
T
B
M)
p
r
ep
r
o
c
ess
in
g
o
f
MRI
d
ata.
T
h
eir
s
tu
d
y
u
s
ed
th
e
d
ee
p
,
co
m
p
ac
t
b
l
o
ck
-
b
ased
Xce
p
tio
n
ar
ch
itectu
r
eb
ased
d
ee
p
lear
n
i
n
g
m
eth
o
d
,
y
ield
i
n
g
a
s
ig
n
if
i
ca
n
t
d
eg
r
ee
o
f
p
r
ec
is
io
n
in
d
etec
tin
g
ea
r
ly
-
s
tag
e
Alzh
eim
er
’
s
d
is
ea
s
e.
Ho
wev
er
,
th
is
s
tu
d
y
d
id
n
o
t
d
ir
ec
tly
ad
d
r
ess
is
s
u
es
r
elate
d
to
d
ata
s
et
u
n
p
r
ed
ictab
ilit
y
,
o
v
er
f
itti
n
g
,
an
d
t
h
e
ch
allen
g
es
ass
o
ciate
d
with
ex
tr
ac
tin
g
im
ag
e
ch
ar
ac
ter
is
tics
f
r
o
m
T
B
M.
B
ag
lat
et
a
l.
[
1
9
]
c
o
n
s
tr
u
cte
d
h
y
b
r
id
m
ac
h
in
e
lear
n
in
g
m
o
d
els
u
s
in
g
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es
(
SVM)
,
r
an
d
o
m
f
o
r
ests
,
an
d
l
o
g
is
tic
r
eg
r
ess
io
n
to
d
etec
t
Alzh
eim
er
’
s
d
is
ea
s
e.
T
h
eir
m
o
d
e
ls
u
s
ed
MRI
p
atien
t
p
ictu
r
es
d
er
iv
ed
f
r
o
m
th
e
OASI
S
d
ataset.
Fu
’
ad
ah
et
a
l.
[
2
0
]
co
n
s
tr
u
cted
a
class
if
icatio
n
m
o
d
el
u
s
in
g
a
C
NN
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
A
n
o
ve
l c
o
n
vo
lu
tio
n
a
l n
e
u
r
a
l
n
etw
o
r
k
a
r
ch
itectu
r
e
fo
r
A
lz
h
e
imer’s d
is
ea
s
e
…
(
S
u
h
a
ila
A
b
u
o
w
a
id
a
)
3521
th
at
was
b
u
ilt
u
p
o
n
th
e
ar
c
h
itectu
r
e
o
f
Alex
Net.
T
h
ey
attain
ed
a
9
5
%
ac
cu
r
ac
y
r
ate
b
y
u
s
in
g
a
c
o
llectio
n
o
f
MRI
p
ictu
r
es
lin
k
ed
to
Alzh
ei
m
er
’
s
d
is
ea
s
e.
Mu
r
u
g
an
et
a
l.
[
2
1
]
d
ev
is
ed
a
C
NN
m
o
d
el
s
p
ec
if
ically
tailo
r
ed
to
d
etec
t
an
d
ca
teg
o
r
ize
in
s
tan
ce
s
o
f
Alzh
eim
er
’
s
d
is
ea
s
e.
T
h
eir
p
r
o
p
o
s
ed
m
o
d
el
co
n
s
is
ted
o
f
two
co
n
v
o
l
u
tio
n
al
lay
er
s
,
o
n
e
m
a
x
-
p
o
o
lin
g
lay
e
r
,
an
d
f
o
u
r
d
e
m
en
tia
n
etwo
r
k
b
lo
c
k
s
.
W
h
en
ev
alu
ated
u
s
in
g
th
e
ADNI
MRI
im
ag
in
g
d
ataset,
it
attain
ed
a
p
r
ec
is
io
n
o
f
9
5
.
2
3
%.
R
allab
an
d
i
et
a
l.
[
2
2
]
d
e
v
i
s
ed
a
m
eth
o
d
th
at
em
p
lo
y
s
th
e
ADNI
d
atab
ase
t
o
id
en
tif
y
an
d
ca
teg
o
r
ize
Alz
h
eim
er
’
s
d
is
ea
s
e
in
el
d
er
ly
i
n
d
iv
id
u
als
with
in
tact
co
g
n
itiv
e
f
u
n
ctio
n
.
T
h
eir
m
o
d
el
ac
h
iev
ed
a
7
5
% a
cc
u
r
ac
y
r
a
te
.
T
o
im
p
r
o
v
e
th
e
ac
cu
r
ac
y
o
f
f
in
d
in
g
Alzh
eim
er
’
s
d
is
ea
s
e,
th
is
m
o
d
el
u
s
es
weig
h
t
r
an
d
o
m
izatio
n
,
d
ee
p
f
ea
t
u
r
e
co
n
ca
ten
atio
n
,
a
n
d
g
r
ad
ien
t
-
weig
h
ted
class
ac
tiv
atio
n
m
ap
p
in
g
.
B
am
b
er
a
n
d
Vis
h
v
ak
ar
m
a
[
2
3
]
d
ev
elo
p
e
d
a
C
NN
to
class
if
y
Alzh
eim
er
’
s
d
is
ea
s
e
in
m
ed
ical
im
ag
e
p
atch
es.
C
NN
u
s
ed
a
s
u
p
er
f
icial
co
n
v
o
l
u
tio
n
al
lay
er
an
d
ac
h
ie
v
ed
a
p
r
ec
is
io
n
r
ate
o
f
9
8
%.
T
h
e
m
o
d
if
ied
I
n
ce
p
tio
n
V3
m
o
d
el
cr
ea
ted
b
y
[
2
4
]
was
v
er
y
g
o
o
d
at
p
u
ttin
g
b
r
ai
n
MRIs
in
to
g
r
o
u
p
s
th
at
s
h
o
w
ed
th
e
d
if
f
er
en
t
s
tag
es
o
f
Alzh
eim
er
’
s
d
is
ea
s
e.
I
t
s
u
r
p
ass
ed
co
n
v
e
n
tio
n
al
m
eth
o
d
s
,
ac
h
iev
in
g
a
test
ac
cu
r
ac
y
o
f
9
8
.
6
7
%.
So
m
e
o
f
th
e
latest
wo
r
k
s
h
a
v
e
s
h
o
w
n
h
o
w
T
B
M
in
teg
r
ated
with
d
ee
p
lear
n
in
g
ca
n
id
en
tif
y
v
o
l
u
m
es
o
f
d
if
f
er
en
ce
s
in
AD
b
y
m
ea
s
u
r
in
g
tis
s
u
e
v
o
lu
m
es
o
f
ce
r
tain
p
ar
ts
o
f
th
e
b
r
ain
s
u
c
h
as
th
e
h
ip
p
o
ca
m
p
u
s
an
d
tem
p
o
r
al
lo
b
e
with
a
n
ac
cu
r
ac
y
r
ate
o
f
u
p
to
9
3
%
[
2
5
]
.
Usi
n
g
R
esNet
-
1
8
f
o
r
f
ea
tu
r
e
ex
t
r
ac
tio
n
an
d
with
co
m
b
in
ed
I
MPA
-
MSVM
AD
cla
s
s
if
icatio
n
ac
cu
r
ac
y
o
f
u
p
to
9
8
.
4
3
% wa
s
o
b
tain
ed
u
s
in
g
th
e
ADNI
d
ata
s
et
[
2
6
]
.
3.
M
E
T
H
O
D
T
h
is
p
ap
er
i
n
tr
o
d
u
ce
s
an
in
n
o
v
ativ
e
d
ee
p
r
esid
u
al
an
d
r
e
g
io
n
al
C
NN
ar
ch
itectu
r
e
f
o
r
th
e
au
to
m
ated
class
if
icatio
n
o
f
Alzh
eim
er
’
s
d
is
ea
s
e
b
ased
o
n
MRI
d
ata.
S
ev
er
al
well
-
k
n
o
wn
p
e
r
f
o
r
m
an
ce
m
etr
ics
ar
e
u
s
ed
to
test
h
o
w
well
th
e
p
r
o
p
o
s
ed
class
if
icat
io
n
m
eth
o
d
wo
r
k
s
,
an
d
th
e
r
esu
lts
ar
e
co
m
p
ar
ed
t
o
th
o
s
e
o
f
ex
is
tin
g
C
NNs.
An
in
cr
ea
s
e
in
th
e
n
u
m
b
er
o
f
tr
ain
i
n
g
ex
am
p
les
in
th
e
ex
p
er
im
en
tal
s
etu
p
m
a
y
lead
to
im
p
r
o
v
ed
g
en
er
aliza
tio
n
.
Fig
u
r
e
1
p
r
esen
ts
a
s
u
cc
in
ct
an
d
th
o
r
o
u
g
h
r
ep
r
esen
tatio
n
o
f
t
h
e
r
ec
o
g
n
ized
class
if
icatio
n
m
eth
o
d
f
o
r
Alzh
eim
er
’
s
d
is
ea
s
e.
Fig
u
r
e
1.
T
h
e
d
etailed
ar
ch
itec
tu
r
e
of
th
e
s
u
g
g
ested
ap
p
r
o
ac
h
f
o
r
class
if
y
in
g
f
o
r
Alzh
eim
er
’
s
d
i
s
e
as
e
ass
o
ciate
d
with
Alzh
eim
er
’
s
d
i
s
e
as
e
3
.
1
.
T
he
d
a
t
a
s
e
t
T
h
is
wo
r
k
u
s
ed
d
atasets
f
r
o
m
r
ep
u
tab
le
o
r
g
a
n
izatio
n
s
s
u
ch
as
Kag
g
le,
ADNI
3
7
,
an
d
OASI
S
3
8
,
wh
ich
ar
e
ac
ce
s
s
ib
le
f
o
r
r
esear
ch
an
d
in
s
tr
u
ctio
n
al
p
u
r
p
o
s
es.
T
h
e
MRI
ADNI
d
ataset
co
m
p
r
is
es
th
e
MRI
s
ca
n
s
u
s
ed
in
th
is
in
v
esti
g
atio
n
.
T
h
e
ADNI
d
ataset
in
clu
d
es
in
d
iv
id
u
al
s
d
iag
n
o
s
ed
with
Alzh
eim
er
’
s
d
is
ea
s
e,
MCI
,
an
d
h
ea
lth
y
co
n
t
r
o
ls
.
T
h
e
ADNI
co
llectio
n
in
clu
d
es
g
e
n
etic
d
ata,
co
g
n
itiv
e
ass
ess
m
en
ts
,
b
lo
o
d
a
n
d
C
SF
b
io
m
ar
k
e
r
s
,
MRI
an
d
PET
s
ca
n
s
,
an
d
clin
ical
in
f
o
r
m
atio
n
.
3
.
2
.
Dev
el
o
pm
ent
of
deep
Res
-
B
RNe
t
c
a
t
e
g
o
r
i
z
a
t
i
o
n
F
o
r
t
h
is
i
n
v
es
t
i
g
at
i
o
n
,
w
e
u
s
ed
C
NN
s
t
o
a
c
q
u
i
r
e
k
n
o
w
l
e
d
g
e
a
b
o
u
t
t
h
e
M
R
I
p
at
t
e
r
n
s
as
s
o
c
i
a
t
e
d
w
i
t
h
A
l
z
h
e
i
m
e
r
’
s
d
is
e
a
s
e
.
T
h
e
c
a
p
ac
i
t
y
o
f
C
N
Ns
t
o
a
c
q
u
i
r
e
k
n
o
wle
d
g
e
a
b
o
u
t
i
m
a
g
e
f
e
a
t
u
r
es
a
n
d
p
a
t
t
e
r
n
s
s
e
r
v
e
s
a
s
a
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
.
3
,
J
u
n
e
20
25
:
3
5
1
9
-
3
5
2
6
3522
d
r
i
v
i
n
g
f
o
r
c
e
f
o
r
t
h
e
i
r
u
s
e
i
n
t
a
s
k
s
s
u
c
h
a
s
c
la
s
s
i
f
i
ca
t
i
o
n
a
n
d
i
d
e
n
t
i
f
i
c
at
i
o
n
.
C
NN
s
a
r
e
w
i
d
e
l
y
u
s
e
d
t
o
e
x
t
r
a
c
t
f
e
a
t
u
r
e
s
a
n
d
c
a
te
g
o
r
i
z
e
d
a
t
a
,
m
o
s
t
l
y
d
u
e
t
o
t
h
e
i
r
l
e
a
r
n
i
n
g
c
a
p
a
c
i
t
y
.
O
u
r
s
t
u
d
y
p
r
e
s
e
n
t
e
d
R
e
s
-
B
R
N
et
,
C
N
N
a
r
c
h
i
t
e
c
t
u
r
e
t
h
at
u
t
i
li
z
e
s
b
o
u
n
d
a
r
y
a
n
d
r
e
g
i
o
n
-
b
a
s
e
d
t
e
c
h
n
i
q
u
e
s
t
o
a
c
c
u
r
a
t
e
l
y
d
e
t
ec
t
A
lz
h
e
i
m
er
’
s
d
i
s
e
as
e
i
n
b
r
a
i
n
M
R
I
d
at
a
.
T
h
e
p
r
o
p
o
s
e
d
m
e
th
o
d
o
l
o
g
y
e
x
t
r
a
c
t
s
i
n
f
o
r
m
a
t
i
o
n
a
b
o
u
t
A
l
z
h
ei
m
e
r
’
s
d
i
s
e
a
s
e
p
at
t
e
r
n
s
d
i
r
e
c
tl
y
f
r
o
m
M
R
I
s
ca
n
s
,
c
o
v
e
r
i
n
g
t
h
e
e
n
t
i
r
e
p
r
o
c
e
s
s
.
T
h
e
f
i
n
a
l
cl
a
s
s
i
f
ic
a
ti
o
n
s
a
r
e
p
e
r
f
o
r
m
e
d
u
s
i
n
g
t
h
e
l
a
s
t
f
u
l
l
y
c
o
n
n
e
c
t
ed
l
a
y
e
r
s
a
n
d
S
o
f
t
Ma
x
-
b
a
s
e
d
o
p
er
a
t
i
o
n
s
o
f
t
h
e
r
e
c
o
m
m
e
n
d
e
d
C
N
N
s
.
C
o
n
v
e
n
ti
o
n
a
l
i
m
a
g
e
p
r
o
c
e
s
s
i
n
g
t
ec
h
n
i
q
u
e
s
[
2
7
]
,
[
2
8
]
h
a
d
a
n
i
m
p
a
c
t
o
n
t
h
e
d
e
s
i
g
n
o
f
R
es
-
B
R
Ne
t
.
A
d
d
i
n
g
c
o
n
v
o
l
u
t
i
o
n
o
p
e
r
a
t
o
r
s
t
o
t
h
e
p
r
o
p
o
s
e
d
f
r
a
m
e
w
o
r
k
i
m
p
r
o
v
e
s
t
h
e
p
e
r
f
o
r
m
a
n
c
e
o
f
r
e
g
i
o
n
a
n
d
b
o
u
n
d
a
r
y
-
b
a
s
e
d
o
p
e
r
a
t
o
r
s
s
o
t
h
a
t
p
a
tt
e
r
n
s
o
f
A
l
z
h
e
i
m
e
r
’
s
d
is
e
as
e
c
a
n
b
e
c
a
p
t
u
r
e
d
m
o
r
e
a
c
c
u
r
a
t
e
l
y
.
Fi
g
u
r
e
2
i
l
l
u
s
t
r
a
t
es
h
o
w
w
e
u
s
e
s
p
a
t
i
al
a
n
d
r
e
s
i
d
u
al
b
l
o
c
k
s
as
in
i
t
i
a
l
c
o
m
p
o
n
e
n
ts
t
o
d
e
m
o
n
s
t
r
a
t
e
t
h
e
r
o
l
e
o
f
a
r
e
a
u
n
i
f
o
r
m
i
t
y
a
n
d
b
o
u
n
d
a
r
y
-
r
e
l
a
t
e
d
f
a
c
t
o
r
s
i
n
h
e
l
p
i
n
g
C
NN
s
d
et
e
c
t
p
a
t
t
e
r
n
s
C
o
m
p
a
r
e
d
t
o
s
p
a
t
i
al
b
l
o
c
k
l
ea
r
n
i
n
g
,
r
e
s
i
d
u
a
l
l
e
a
r
n
i
n
g
le
ts
t
h
e
a
l
g
o
r
i
t
h
m
t
a
k
e
i
n
t
o
a
c
co
u
n
t
s
m
a
l
l
c
h
a
n
g
e
s
i
n
t
e
x
t
u
r
e
a
n
d
c
o
n
t
r
a
s
t,
g
e
t
a
r
o
u
n
d
t
h
e
p
r
o
b
l
e
m
o
f
v
a
n
i
s
h
i
n
g
g
r
a
d
i
e
n
t
s
,
a
n
d
i
m
p
r
o
v
e
t
h
e
l
e
a
r
n
e
d
f
e
a
t
u
r
e
m
a
p
s
a
n
d
a
l
g
o
r
i
t
h
m
c
o
n
v
e
r
g
e
n
c
e
.
T
h
e
R
e
s
-
B
R
N
et
a
r
c
h
i
t
e
ctu
r
e
c
o
m
p
r
i
s
e
s
a
n
i
n
it
i
a
l
s
e
t
o
f
t
h
r
e
e
s
p
a
t
ia
l
b
l
o
c
k
s
,
f
o
l
l
o
w
e
d
b
y
f
o
u
r
r
e
s
i
d
u
a
l
b
lo
c
k
s
.
E
a
c
h
s
p
a
t
i
al
b
l
o
c
k
c
o
m
p
r
i
s
e
s
b
a
t
c
h
n
o
r
m
a
l
iz
a
t
i
o
n
,
R
e
L
U
,
a
n
d
a
s
i
n
g
l
e
c
o
n
v
o
l
u
t
i
o
n
l
a
y
e
r
.
T
h
e
R
e
L
U
f
u
n
c
t
i
o
n
s
e
r
v
e
s
a
s
a
n
a
c
t
i
v
at
i
o
n
f
u
n
c
t
i
o
n
,
w
h
e
r
e
a
s
t
h
e
c
o
n
v
o
l
u
t
i
o
n
l
a
y
e
r
e
x
p
l
o
i
t
s
p
a
t
t
e
r
n
s
r
e
l
at
e
d
t
o
A
l
z
h
ei
m
e
r
’
s
i
l
l
n
e
s
s
.
A
f
t
e
r
e
ac
h
s
p
a
t
ia
l
b
l
o
c
k
,
a
m
a
x
-
p
o
o
l
i
n
g
o
r
a
v
e
r
a
g
e
-
p
o
o
l
i
n
g
o
p
e
r
a
t
i
o
n
i
s
p
e
r
f
o
r
m
e
d
t
o
a
s
s
es
s
t
h
e
a
r
e
a
’
s
u
n
i
f
o
r
m
i
t
y
a
n
d
i
d
e
n
t
i
f
y
A
l
z
h
eim
e
r
’
s
d
i
s
ea
s
e
f
ea
t
u
r
e
s
c
o
n
n
e
c
te
d
t
o
b
o
u
n
d
a
r
i
e
s
.
R
esid
u
al
lear
n
in
g
allo
ws
th
e
m
o
d
el
t
o
s
o
lv
e
th
e
v
a
n
is
h
in
g
g
r
a
d
ien
t
p
r
o
b
lem
,
ac
co
u
n
t
f
o
r
m
i
n
o
r
ch
an
g
es
in
tex
tu
r
e
a
n
d
co
n
tr
a
s
t,
an
d
en
h
an
ce
f
ea
t
u
r
e
m
ap
r
ep
r
esen
tatio
n
an
d
c
o
n
v
e
r
g
en
c
e
d
u
r
in
g
tr
ai
n
in
g
as
co
m
p
ar
ed
t
o
s
p
atial
b
lo
ck
le
ar
n
in
g
.
C
o
n
v
en
tio
n
al
p
lain
co
n
v
o
l
u
tio
n
al
b
lo
c
k
s
an
d
r
es
id
u
al
b
lo
ck
s
d
if
f
er
m
o
s
tly
in
Fig
u
r
e
2
.
Fig
u
r
e
2
(
a
)
s
p
ec
if
ically
d
is
p
lay
s
th
e
ar
ch
itectu
r
e
o
f
a
p
lain
c
o
n
v
o
l
u
tio
n
al
b
lo
ck
in
wh
ich
co
n
s
ec
u
tiv
e
in
p
u
ts
ar
e
s
u
cc
es
s
iv
ely
r
o
u
ted
th
r
o
u
g
h
co
n
v
o
l
u
tio
n
al
an
d
ac
tiv
atio
n
lay
er
s
d
ev
o
id
o
f
s
h
o
r
tcu
t
co
n
n
ec
tio
n
s
.
B
y
m
ea
n
s
o
f
s
k
i
p
co
n
n
ec
tio
n
s
th
at
en
ab
le
g
r
a
d
ien
ts
to
g
o
s
tr
aig
h
t
to
ea
r
lier
lay
er
s
,
Fig
u
r
e
2
(
b
)
s
h
o
ws
th
e
s
tr
u
ctu
r
e
o
f
a
r
esid
u
al
b
lo
c
k
,
th
e
r
ef
o
r
e
en
a
b
lin
g
d
ee
p
e
r
n
etwo
r
k
tr
ai
n
in
g
a
n
d
im
p
r
o
v
in
g
f
ea
tu
r
e
ex
tr
ac
tio
n
ca
p
ac
ity
f
o
r
Alzh
eim
er
'
s
-
r
elate
d
p
atter
n
s
.
Fig
u
r
e
2.
T
h
e
d
if
f
er
e
n
ce
in
t
h
e
p
r
o
ce
s
s
of
(
a)
p
lain
an
d
(
b
)
r
e
s
id
u
al
b
l
o
c
k
s
4.
RE
SU
L
T
AN
D
D
I
S
CU
SS
I
O
N
T
h
is
p
ap
er
p
r
o
p
o
s
es
u
s
in
g
a
C
NN
s
y
s
tem
to
ac
cu
r
ately
id
en
tify
p
eo
p
le
with
Alzh
eim
er
’
s
d
is
ea
s
e
b
ased
o
n
MRI
s
ca
n
s
.
W
e
co
n
d
u
cte
d
test
s
to
ev
alu
ate
t
h
e
p
r
o
p
o
s
ed
a
p
p
r
o
ac
h
e
m
p
ir
ically
.
An
o
v
e
r
all
ev
alu
atio
n
o
f
Alzh
eim
e
r
’
s
MRI
class
if
icatio
n
is
co
n
d
u
cted
b
y
co
m
p
a
r
in
g
th
e
p
e
r
f
o
r
m
a
n
c
e
s
with
a
s
tate
-
of
-
th
e
-
ar
t
m
o
d
el.
T
h
e
R
es
-
B
R
Ne
t
m
o
d
el
in
th
is
wo
r
k
was
tr
ain
ed
with
a
lear
n
in
g
r
ate
o
f
0
.
0
0
1
,
with
a
b
atch
s
ize
of
32,
an
d
it
was
tr
ain
ed
f
o
r
50
ep
o
ch
s
.
T
h
e
latter
u
s
ed
th
e
Ad
am
o
p
tim
izer
an
d
cr
o
s
s
-
en
t
r
o
p
y
lo
s
s
.
T
o
av
o
id
th
e
ca
s
e
o
f
o
v
er
f
itti
n
g
f
o
r
th
e
lay
er
s
with
a
f
u
lly
c
o
n
n
ec
ted
n
atu
r
e,
a
d
r
o
p
o
u
t r
ate
o
f
0
.
3
wa
s
i
m
p
l
e
m
e
n
t
e
d
.
4
.
1
.
A
n
a
l
y
s
is
o
f
P
e
r
f
o
r
ma
n
c
e
w
i
t
h
a
s
t
a
t
e
-
of
-
t
h
e
-
a
r
t
mo
d
e
l
T
h
e
p
r
o
p
o
s
ed
R
es
-
B
R
Net
m
o
d
el
h
as
b
ee
n
q
u
an
titativ
ely
c
o
m
p
ar
ed
with
th
e
ea
r
lier
s
tu
d
ie
s
th
at
ar
e
s
u
m
m
ar
ized
in
T
a
b
le
1.
Fro
m
th
e
tab
le,
we
ca
n
s
ee
th
at
R
es
-
B
R
Net
h
as
a
b
etter
p
er
f
o
r
m
an
ce
in
i
d
en
tify
in
g
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
A
n
o
ve
l c
o
n
vo
lu
tio
n
a
l n
e
u
r
a
l
n
etw
o
r
k
a
r
ch
itectu
r
e
fo
r
A
lz
h
e
imer’s d
is
ea
s
e
…
(
S
u
h
a
ila
A
b
u
o
w
a
id
a
)
3523
s
p
ec
if
ic
p
atter
n
s
ass
o
ciate
d
with
MRI
f
o
r
Alzh
eim
er
’
s
d
is
ea
s
e
class
if
icatio
n
task
s
.
T
h
is
s
u
p
er
io
r
ity
is
ass
ess
ed
b
y
m
ea
n
s
o
f
s
tan
d
ar
d
p
er
f
o
r
m
an
ce
in
d
icato
r
s
,
s
u
ch
as
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e.
T
h
u
s
,
co
m
p
a
r
ed
to
th
e
b
asic
R
esNet
s
tr
u
ctu
r
e,
en
o
u
g
h
r
ea
s
o
n
s
ex
is
t
to
ex
p
lain
wh
y
th
e
R
es
-
B
R
Net
m
o
d
el
g
ain
s
h
ig
h
er
p
er
f
o
r
m
a
n
ce
in
ar
ch
itectu
r
al
d
esig
n
.
T
h
e
m
o
d
el
u
s
es
h
ig
h
-
le
v
el
co
n
v
o
lu
ti
o
n
al
o
p
er
atio
n
s
th
at
s
u
p
er
s
ed
e
f
ea
tu
r
e
e
x
tr
ac
tio
n
i
n
th
at
it
tak
es
in
to
co
n
s
id
er
ati
o
n
lo
ca
l
an
d
g
lo
b
al
s
p
atial
h
ie
r
ar
ch
ies
o
f
th
e
in
p
u
t
MRI
im
ag
es.
T
ab
le
1.
A
p
er
f
o
r
m
an
ce
g
ain
of
th
e
d
ev
elo
p
ed
R
es
-
B
R
Net
co
m
p
ar
ed
to
a
s
tate
-
of
-
th
e
-
ar
t
m
o
d
e
l
A
u
t
h
o
r
s
M
e
t
h
o
d
o
l
o
g
y
A
c
c
u
r
a
c
y
[
1
7
]
V
G
G
N
e
t
9
6
.
0
8
%
[
2
9
]
3D
C
N
N
9
3
.
0
0
%
[
2
1
]
C
N
N
9
5
.
2
3
%
[
2
3
]
C
N
N
9
8
%
[
2
4
]
I
n
c
e
p
t
i
o
n
V3
(
F
i
n
e
t
u
n
i
n
g
)
9
8
.
6
8
%
P
r
o
p
o
se
d
w
o
r
k
Res
-
B
R
N
e
t
9
9
.
2
2
%
Mo
r
e
s
p
ec
if
ically
,
R
es
-
B
R
Ne
t
in
clu
d
es
a
r
esid
u
al
co
n
n
ec
tio
n
th
at
s
o
lv
es
th
e
v
an
is
h
in
g
g
r
a
d
ien
t
is
s
u
e
an
d
en
ab
les
th
e
au
th
o
r
s
to
r
ea
lize
d
ee
p
er
n
etwo
r
k
s
.
I
n
ad
d
itio
n
to
th
is
,
th
e
co
n
s
is
ten
t
in
teg
r
atio
n
o
f
b
atch
n
o
r
m
aliza
tio
n
lay
e
r
s
m
ak
es
th
e
tr
ain
in
g
p
r
o
ce
s
s
more
s
tab
le
an
d
f
aster
.
Oth
er
im
p
o
r
tan
t
im
p
r
o
v
em
e
n
ts
in
clu
d
e
th
e
d
eter
m
in
atio
n
of
a
well
-
b
alan
ce
d
h
y
p
er
p
ar
a
m
eter
in
clu
d
i
n
g
th
e
lear
n
in
g
r
ate,
th
e
b
atch
s
ize
an
d
th
e
d
r
o
p
o
u
t
f
ac
to
r
s
wh
ich
m
in
im
ize
o
v
er
f
itti
n
g
an
d
in
cr
ea
s
e
th
e
g
e
n
er
a
lity
o
f
th
e
m
o
d
el.
Fu
r
th
e
r
m
o
r
e,
m
eth
o
d
s
s
u
ch
as
au
g
m
en
tin
g
d
ata
h
elp
e
d
ac
h
ie
v
e
th
e
g
o
al
o
f
b
ein
g
in
v
ar
ia
n
t
to
v
ar
iab
ilit
y
in
th
e
MRI
d
atas
ets,
wh
ich
was
also
h
ig
h
ly
ef
f
ec
tiv
e.
T
h
e
f
o
llo
win
g
r
esu
lts
ar
e
o
b
t
ain
ed
as
in
d
icate
d
in
T
ab
le
1,
th
e
p
r
o
p
o
s
ed
R
es
-
B
R
Ne
t
wh
ich
is
a
b
r
ea
k
th
r
o
u
g
h
ac
h
iev
es
an
ac
cu
r
ac
y
of
9
9
.
2
2
%
o
u
t
co
m
p
in
g
o
th
er
m
eth
o
d
s
.
T
h
is
r
esu
lt
s
tr
en
g
th
en
s
th
e
m
o
d
el’
s
ab
ilit
y
to
ex
tr
ac
t
d
is
cr
im
in
ativ
e
f
ea
tu
r
es
r
elate
d
to
MRI
s
ca
n
s
of
AD
as
well
as
am
p
lifie
s
its
ef
f
ec
tiv
en
ess
as
a
r
eliab
le
d
iag
n
o
s
tic
to
o
l
f
o
r
Alzh
eim
er
’
s
d
is
ea
s
e.
Fig
u
r
e
3
s
h
o
ws
b
r
ain
m
ag
n
etic
r
eso
n
an
ce
im
ag
es
th
at
ju
x
tap
o
s
e
th
e
a
n
ticip
ated
class
es
with
th
e
ac
tu
al
class
es
d
er
iv
ed
f
r
o
m
th
e
test
d
ata.
As
s
h
o
wn
in
Fig
u
r
e
3,
th
e
C
NN
th
at
was
cr
ea
ted
ex
h
ib
it
s
a
s
m
o
o
th
a
n
d
r
ap
id
c
o
n
v
e
r
g
en
ce
to
war
d
its
id
ea
l
v
alu
e.
T
h
e
lik
ely
ca
u
s
es
o
f
m
is
d
iag
n
o
s
is
in
clu
d
e
lo
w
co
n
tr
ast,
u
n
ev
en
s
am
p
le
p
atter
n
s
,
an
d
v
ar
iab
le
lig
h
t
f
lu
ctu
atio
n
s
.
C
N
N
ar
e
u
s
ed
to
en
h
an
ce
th
e
g
en
er
aliza
tio
n
an
d
r
esil
ien
ce
o
f
test
s
am
p
les.
Fig
u
r
e
3
.
E
x
am
in
in
g
wh
eth
er
t
h
e
p
r
ed
icted
lab
el
m
atch
e
d
th
e
r
ea
l la
b
el
o
r
n
o
t
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
.
3
,
J
u
n
e
20
25
:
3
5
1
9
-
3
5
2
6
3524
5.
C
O
N
CL
US
I
O
N
E
ar
ly
d
etec
tio
n
of
Alzh
eim
er
’
s
d
is
ea
s
e
is
ess
en
tia
l
f
o
r
p
atien
t
r
ec
o
v
er
y
.
C
o
n
s
eq
u
en
tly
,
a
n
o
v
el
tailo
r
ed
d
ee
p
C
NN
m
o
d
el
is
cr
ea
ted
in
th
is
s
tu
d
y
to
ca
teg
o
r
ize
b
r
ain
MRI
d
ata
ass
o
ciate
d
with
Alzh
eim
er
’
s
d
is
ea
s
e.
T
h
e
cr
ea
ted
R
es
-
B
R
Net’
s
r
eg
io
n
al
an
d
b
o
u
n
d
ar
y
o
p
er
ato
r
s
ar
e
u
s
ef
u
l
f
o
r
teac
h
in
g
d
is
cr
im
in
ativ
e
ch
ar
ac
ter
is
tics
to
th
e
s
u
g
g
ested
m
o
d
el.
T
h
e
R
es
-
B
R
Net
th
at
wa
s
m
ad
e
al
s
o
u
s
es
s
p
atial
an
d
r
esid
u
al
id
ea
s
to
g
et
f
ea
tu
r
e
m
ap
s
with
a
lo
t
o
f
d
if
f
e
r
en
t
k
i
n
d
s
o
f
in
f
o
r
m
atio
n
.
T
h
is
m
ak
es
i
t
ea
s
ier
to
u
n
d
er
s
tan
d
th
e
h
o
m
o
g
en
eity
,
tex
tu
r
al
v
ar
iatio
n
,
an
d
s
tr
u
ctu
r
al
p
atter
n
s
th
at
ar
e
lin
k
ed
to
Alzh
eim
e
r
’
s
d
is
ea
s
e.
Usi
n
g
th
e
cu
r
r
en
t d
ee
p
C
NN
m
o
d
els,
th
e
b
u
ild
s
y
s
tem
’
s
p
er
f
o
r
m
a
n
ce
is
ex
am
in
ed
.
T
h
e
s
u
g
g
ested
R
es
-
B
R
Net
p
er
f
o
r
m
s
b
etter
th
an
cu
r
r
e
n
t
C
NN
d
esig
n
s
,
ac
co
r
d
in
g
to
th
e
e
x
p
e
r
im
en
t
f
in
d
i
n
g
s
,
s
u
g
g
esti
n
g
an
in
cr
ea
s
e
in
ac
cu
r
ac
y
.
T
h
e
s
u
g
g
ested
m
eth
o
d
will
p
r
o
b
a
b
ly
m
ak
e
it e
asier
f
o
r
m
e
d
ical
p
r
ac
titi
o
n
er
s
to
id
e
n
tify
Alzh
eim
er
’
s
d
is
ea
s
e
in
th
e
b
r
a
in
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
e
Dea
n
s
h
ip
o
f
Scien
tific
R
esear
ch
at
Z
ar
q
a
Un
i
v
er
s
ity
,
J
o
r
d
an
f
u
n
d
s
th
is
r
esear
ch
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT)
to
r
ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
Su
h
aila
Ab
u
o
waid
a
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Z
aid
Mu
s
taf
a
✓
✓
✓
✓
✓
✓
✓
Ah
m
ad
Ab
u
r
o
m
m
a
n
✓
✓
✓
✓
✓
✓
✓
Naw
af
Als
h
d
aif
at
✓
✓
✓
✓
✓
✓
✓
✓
✓
Mu
s
ab
I
q
tait
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
r
mal
a
n
a
l
y
s
i
s
I
:
I
n
v
e
s
t
i
g
a
t
i
o
n
R
:
R
e
so
u
r
c
e
s
D
:
D
a
t
a
C
u
r
a
t
i
o
n
O
:
W
r
i
t
i
n
g
-
O
r
i
g
i
n
a
l
D
r
a
f
t
E
:
W
r
i
t
i
n
g
-
R
e
v
i
e
w
&
E
d
i
t
i
n
g
Vi
:
Vi
su
a
l
i
z
a
t
i
o
n
Su
:
Su
p
e
r
v
i
s
i
o
n
P
:
P
r
o
j
e
c
t
a
d
mi
n
i
st
r
a
t
i
o
n
Fu
:
Fu
n
d
i
n
g
a
c
q
u
i
si
t
i
o
n
CO
NF
L
I
C
T
O
F
I
N
T
E
R
E
S
T
ST
A
T
E
M
E
NT
T
h
e
au
t
h
o
r
s
d
ec
lar
e
th
at
th
e
y
h
av
e
n
o
k
n
o
wn
c
o
m
p
etin
g
f
in
an
cial
in
ter
ests
o
r
p
er
s
o
n
al
r
el
atio
n
s
h
ip
s
th
at
co
u
ld
h
av
e
ap
p
ea
r
ed
t
o
in
f
lu
en
ce
th
e
wo
r
k
r
e
p
o
r
te
d
in
t
h
is
p
ap
er
.
DATA AV
AI
L
AB
I
L
I
T
Y
T
h
e
d
ata
t
h
at
s
u
p
p
o
r
t
t
h
e
f
in
d
in
g
s
o
f
th
is
s
tu
d
y
a
r
e
o
p
en
ly
av
ailab
le
f
r
o
m
th
e
Alzh
eim
er
’
s
Dis
ea
s
e
Neu
r
o
im
ag
in
g
I
n
itiativ
e
(
AD
NI
)
at
h
ttp
s
:
//a
d
n
i.lo
n
i
.
u
s
c.
ed
u
,
th
e
OASI
S
B
r
ain
Pro
ject
at
h
ttp
s
:
//w
w
w
.
o
a
s
is
-
b
r
a
in
s
.
o
r
g
,
an
d
Kag
g
le
at
h
ttp
s
:
//w
w
w
.
ka
g
g
le.
co
m
.
T
h
ese
d
atasets
in
clu
d
e
MRI
s
ca
n
s
,
co
g
n
itiv
e
ass
es
s
m
en
ts
,
g
en
etic
d
ata,
an
d
r
elate
d
clin
ic
al
in
f
o
r
m
atio
n
an
d
ar
e
ac
ce
s
s
ib
le
f
o
r
r
esear
ch
an
d
in
s
tr
u
ctio
n
al
p
u
r
p
o
s
es.
RE
F
E
R
E
NC
E
S
[
1
]
S
.
O
d
i
ma
y
o
,
C
.
C
.
O
l
i
sa
h
,
a
n
d
K
.
M
o
h
a
mm
e
d
,
“
S
t
r
u
c
t
u
r
e
f
o
c
u
se
d
n
e
u
r
o
d
e
g
e
n
e
r
a
t
i
o
n
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
f
o
r
mo
d
e
l
l
i
n
g
a
n
d
c
l
a
ss
i
f
i
c
a
t
i
o
n
o
f
A
l
z
h
e
i
mer’
s
d
i
s
e
a
se,
”
S
c
i
e
n
t
i
f
i
c
R
e
p
o
r
t
s
,
v
o
l
.
1
4
,
n
o
.
1
,
Ju
l
.
2
0
2
4
,
d
o
i
:
1
0
.
1
0
3
8
/
s
4
1
5
9
8
-
0
2
4
-
6
0
6
1
1
-
8.
[
2
]
T.
M
.
M
o
h
a
me
d
,
M
.
A
.
M
.
Y
o
u
s
sef,
A
.
A
.
B
a
k
r
y
,
a
n
d
M
.
M
.
E
l
-
K
e
i
y
,
“
A
l
z
h
e
i
m
e
r
’
s
d
i
se
a
se
i
m
p
r
o
v
e
d
t
h
r
o
u
g
h
t
h
e
a
c
t
i
v
i
t
y
o
f
mi
t
o
c
h
o
n
d
r
i
a
l
c
h
a
i
n
c
o
m
p
l
e
x
e
s
a
n
d
t
h
e
i
r
g
e
n
e
e
x
p
r
e
ssi
o
n
i
n
r
a
t
s
b
y
b
o
sw
e
l
l
i
c
a
c
i
d
,
”
Me
t
a
b
o
l
i
c
Br
a
i
n
D
i
se
a
se
,
v
o
l
.
3
6
,
n
o
.
2
,
p
p
.
2
5
5
–
2
6
4
,
N
o
v
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
0
7
/
s1
1
0
1
1
-
020
-
0
0
6
3
9
-
7.
[
3
]
B
.
A
l
-
N
a
a
mi
,
H
.
A
b
u
O
w
i
d
a
,
M
.
A
b
u
M
a
l
l
o
u
h
,
F
.
A
l
-
N
a
i
ma
t
,
M
.
A
g
h
a
,
a
n
d
A
.
-
R
.
A
l
-
H
i
n
n
a
w
i
,
“
A
n
e
w
p
r
o
t
o
t
y
p
e
o
f
smar
t
w
e
a
r
a
b
l
e
m
o
n
i
t
o
r
i
n
g
s
y
st
e
m
s
o
l
u
t
i
o
n
f
o
r
A
l
z
h
e
i
m
e
r
’
s
p
a
t
i
e
n
t
s
,
”
M
e
d
i
c
a
l
D
e
v
i
c
e
s:
Ev
i
d
e
n
c
e
a
n
d
R
e
se
a
rc
h
,
v
o
l
.
1
4
,
p
p
.
4
2
3
–
4
3
3
,
D
e
c
.
2
0
2
1
,
d
o
i
:
1
0
.
2
1
4
7
/
M
D
ER
.
S
3
3
9
8
5
5
.
[
4
]
K
.
Ta
d
o
k
o
r
o
e
t
a
l
.
,
“
E
a
r
l
y
d
e
t
e
c
t
i
o
n
o
f
c
o
g
n
i
t
i
v
e
d
e
c
l
i
n
e
i
n
mi
l
d
c
o
g
n
i
t
i
v
e
i
mp
a
i
r
m
e
n
t
a
n
d
A
l
z
h
e
i
m
e
r
’
s
d
i
se
a
se
w
i
t
h
a
n
o
v
e
l
e
y
e
t
r
a
c
k
i
n
g
t
e
st
,
”
J
o
u
r
n
a
l
o
f
t
h
e
N
e
u
r
o
l
o
g
i
c
a
l
S
c
i
e
n
c
e
s
,
v
o
l
.
4
2
7
,
p
.
1
1
7
5
2
9
,
A
u
g
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
j
n
s.
2
0
2
1
.
1
1
7
5
2
9
.
[
5
]
T.
Zh
a
n
g
e
t
a
l
.
,
“
P
r
e
d
i
c
t
i
n
g
M
C
I
t
o
A
D
c
o
n
v
e
r
sa
t
i
o
n
u
s
i
n
g
i
n
t
e
g
r
a
t
e
d
s
M
R
I
a
n
d
r
s
-
f
M
R
I
:
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
n
d
g
r
a
p
h
t
h
e
o
r
y
a
p
p
r
o
a
c
h
,
”
Fr
o
n
t
i
e
rs
i
n
A
g
i
n
g
N
e
u
r
o
s
c
i
e
n
c
e
,
v
o
l
.
1
3
,
J
u
l
.
2
0
2
1
,
d
o
i
:
1
0
.
3
3
8
9
/
f
n
a
g
i
.
2
0
2
1
.
6
8
8
9
2
6
.
[
6
]
F
.
S
e
g
o
v
i
a
e
t
a
l
.
,
“
M
u
l
t
i
v
a
r
i
a
t
e
a
n
a
l
y
si
s
o
f
d
u
a
l
-
p
o
i
n
t
a
m
y
l
o
i
d
P
E
T
i
n
t
e
n
d
e
d
t
o
a
ss
i
st
t
h
e
d
i
a
g
n
o
s
i
s
o
f
A
l
z
h
e
i
mer’
s
d
i
s
e
a
s
e
,
”
N
e
u
ro
c
o
m
p
u
t
i
n
g
,
v
o
l
.
4
1
7
,
p
p
.
1
–
9
,
D
e
c
.
2
0
2
0
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
n
e
u
c
o
m.
2
0
2
0
.
0
6
.
0
8
1
.
[
7
]
A
.
G
h
a
f
f
a
r
i
-
R
a
f
i
e
t
a
l
.
,
“
T
h
e
r
o
l
e
o
f
mag
n
e
t
i
c
r
e
s
o
n
a
n
c
e
i
ma
g
i
n
g
t
o
i
n
f
o
r
m
c
l
i
n
i
c
a
l
d
e
c
i
s
i
o
n
-
ma
k
i
n
g
i
n
a
c
u
t
e
s
p
i
n
a
l
c
o
r
d
i
n
j
u
r
y
:
A
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
A
n
o
ve
l c
o
n
vo
lu
tio
n
a
l n
e
u
r
a
l
n
etw
o
r
k
a
r
ch
itectu
r
e
fo
r
A
lz
h
e
imer’s d
is
ea
s
e
…
(
S
u
h
a
ila
A
b
u
o
w
a
id
a
)
3525
sy
st
e
ma
t
i
c
r
e
v
i
e
w
a
n
d
me
t
a
-
a
n
a
l
y
si
s,”
J
o
u
rn
a
l
o
f
C
l
i
n
i
c
a
l
Me
d
i
c
i
n
e
,
v
o
l
.
1
0
,
n
o
.
2
1
,
p
.
4
9
4
8
,
O
c
t
.
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
j
c
m1
0
2
1
4
9
4
8
.
[
8
]
S
.
A
b
u
o
w
a
i
d
a
e
t
a
l
.
,
“
E
v
i
d
e
n
c
e
d
e
t
e
c
t
i
o
n
i
n
c
l
o
u
d
f
o
r
e
n
s
i
c
s
:
c
l
a
ss
i
f
y
i
n
g
c
y
b
e
r
-
a
t
t
a
c
k
s
i
n
I
a
a
S
e
n
v
i
r
o
n
m
e
n
t
s
u
s
i
n
g
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
,
”
D
a
t
a
a
n
d
M
e
t
a
d
a
t
a
,
v
o
l
.
4
,
p
.
6
9
9
,
F
e
b
.
2
0
2
5
,
d
o
i
:
1
0
.
5
6
2
9
4
/
d
m2
0
2
5
6
9
9
.
[
9
]
B
.
A
l
‐
n
a
a
mi
,
H
.
F
r
a
i
h
a
t
,
H
.
A
.
O
w
i
d
a
,
K
.
A
l
‐
h
a
m
a
d
,
R
.
D
e
F
a
z
i
o
,
a
n
d
P
.
V
i
sco
n
t
i
,
“
A
u
t
o
ma
t
e
d
d
e
t
e
c
t
i
o
n
o
f
l
e
f
t
b
u
n
d
l
e
b
r
a
n
c
h
b
l
o
c
k
f
r
o
m
E
C
G
s
i
g
n
a
l
u
t
i
l
i
z
i
n
g
t
h
e
max
i
ma
l
o
v
e
r
l
a
p
d
i
s
c
r
e
t
e
w
a
v
e
l
e
t
t
r
a
n
sf
o
r
m
w
i
t
h
A
N
F
I
S
,
”
C
o
m
p
u
t
e
rs
,
v
o
l
.
1
1
,
n
o
.
6
,
p
.
9
3
,
Ju
n
.
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
c
o
m
p
u
t
e
r
s
1
1
0
6
0
0
9
3
.
[
1
0
]
H
.
A
.
O
w
i
d
a
,
A
.
A
l
-
G
h
r
a
i
b
a
h
,
a
n
d
M
.
A
l
t
a
y
e
b
,
“
C
l
a
ssi
f
i
c
a
t
i
o
n
o
f
c
h
e
st
X
-
R
a
y
i
ma
g
e
s
u
si
n
g
w
a
v
e
l
e
t
a
n
d
M
F
C
C
f
e
a
t
u
r
e
s
a
n
d
su
p
p
o
r
t
v
e
c
t
o
r
m
a
c
h
i
n
e
c
l
a
ssi
f
i
e
r
,
”
E
n
g
i
n
e
e
r
i
n
g
,
T
e
c
h
n
o
l
o
g
y
a
n
d
A
p
p
l
i
e
d
S
c
i
e
n
c
e
Re
se
a
r
c
h
,
v
o
l
.
1
1
,
n
o
.
4
,
p
p
.
7
2
9
6
–
7
3
0
1
,
A
u
g
.
2
0
2
1
,
d
o
i
:
1
0
.
4
8
0
8
4
/
e
t
a
sr
.
4
1
2
3
.
[
1
1
]
A
.
E
h
sa
n
e
t
a
l
.
,
“
E
n
h
a
n
c
e
d
a
n
o
m
a
l
y
d
e
t
e
c
t
i
o
n
i
n
e
t
h
e
r
e
u
m
:
u
n
v
e
i
l
i
n
g
a
n
d
c
l
a
ssi
f
y
i
n
g
t
h
r
e
a
t
s
w
i
t
h
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
,
”
I
EE
E
Ac
c
e
ss
,
v
o
l
.
1
2
,
p
p
.
1
7
6
4
4
0
–
1
7
6
4
5
6
,
2
0
2
4
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
2
4
.
3
5
0
4
3
0
0
.
[
1
2
]
Z.
S
a
l
a
h
,
H
.
A
b
u
O
w
i
d
a
,
E.
A
b
u
El
s
o
u
d
,
E.
A
l
h
e
n
a
w
i
,
S
.
A
b
u
o
w
a
i
d
a
,
a
n
d
N
.
A
l
s
h
d
a
i
f
a
t
,
“
A
n
e
f
f
e
c
t
i
v
e
e
n
s
e
m
b
l
e
a
p
p
r
o
a
c
h
f
o
r
p
r
e
v
e
n
t
i
n
g
a
n
d
d
e
t
e
c
t
i
n
g
p
h
i
sh
i
n
g
a
t
t
a
c
k
s
i
n
t
e
x
t
u
a
l
f
o
r
m,”
Fu
t
u
r
e
I
n
t
e
rn
e
t
,
v
o
l
.
1
6
,
n
o
.
1
1
,
p
.
4
1
4
,
N
o
v
.
2
0
2
4
,
d
o
i
:
1
0
.
3
3
9
0
/
f
i
1
6
1
1
0
4
1
4
.
[
1
3
]
M
.
M
o
ma
n
i
e
t
a
l
.
,
“
U
t
i
l
i
z
a
t
i
o
n
o
f
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
i
n
b
r
e
a
st
c
a
n
c
e
r
d
e
t
e
c
t
i
o
n
,
”
i
n
2
0
2
4
2
5
t
h
I
n
t
e
r
n
a
t
i
o
n
a
l
Ar
a
b
C
o
n
f
e
r
e
n
c
e
o
n
I
n
f
o
rm
a
t
i
o
n
T
e
c
h
n
o
l
o
g
y
(
A
C
I
T
)
,
D
e
c
.
2
0
2
4
,
p
p
.
1
–
1
0
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
I
T6
2
8
0
5
.
2
0
2
4
.
1
0
8
7
7
0
5
8
.
[
1
4
]
R
.
A
l
a
z
a
i
d
a
h
,
M
.
A
.
A
l
mai
a
h
,
a
n
d
M
.
A
l
-
L
u
w
a
i
c
i
,
“
A
ss
o
c
i
a
t
i
v
e
c
l
a
ssi
f
i
c
a
t
i
o
n
i
n
mu
l
t
i
-
l
a
b
e
l
c
l
a
ss
i
f
i
c
a
t
i
o
n
:
A
n
i
n
v
e
st
i
g
a
t
i
v
e
st
u
d
y
,
”
J
o
rd
a
n
i
a
n
J
o
u
r
n
a
l
o
f
C
o
m
p
u
t
e
rs
a
n
d
I
n
f
o
rm
a
t
i
o
n
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
7
,
n
o
.
2
,
p
p
.
1
6
6
–
1
7
9
,
2
0
2
1
,
d
o
i
:
1
0
.
5
4
5
5
/
JJCI
T.
7
1
-
1
6
1
5
2
9
7
6
3
4
.
[
1
5
]
B
.
C
h
e
n
g
,
M
.
Li
u
,
D
.
Z
h
a
n
g
,
B
.
C
.
M
u
n
s
e
l
l
,
a
n
d
D
.
S
h
e
n
,
“
D
o
ma
i
n
t
r
a
n
sf
e
r
l
e
a
r
n
i
n
g
f
o
r
M
C
I
c
o
n
v
e
r
s
i
o
n
p
r
e
d
i
c
t
i
o
n
,
”
I
E
EE
T
ra
n
s
a
c
t
i
o
n
s
o
n
B
i
o
m
e
d
i
c
a
l
En
g
i
n
e
e
r
i
n
g
,
v
o
l
.
6
2
,
n
o
.
7
,
p
p
.
1
8
0
5
–
1
8
1
7
,
J
u
l
.
2
0
1
5
,
d
o
i
:
1
0
.
1
1
0
9
/
TB
M
E
.
2
0
1
5
.
2
4
0
4
8
0
9
.
[
1
6
]
F
.
Li
,
L.
Tr
a
n
,
K
.
H
.
T
h
u
n
g
,
S
.
J
i
,
D
.
S
h
e
n
,
a
n
d
J.
Li
,
“
A
r
o
b
u
s
t
d
e
e
p
m
o
d
e
l
f
o
r
i
m
p
r
o
v
e
d
c
l
a
ss
i
f
i
c
a
t
i
o
n
o
f
A
D
/
M
C
I
p
a
t
i
e
n
t
s
,
”
I
EEE
J
o
u
rn
a
l
o
f
Bi
o
m
e
d
i
c
a
l
a
n
d
H
e
a
l
t
h
I
n
f
o
rm
a
t
i
c
s
,
v
o
l
.
1
9
,
n
o
.
5
,
p
p
.
1
6
1
0
–
1
6
1
6
,
S
e
p
.
2
0
1
5
,
d
o
i
:
1
0
.
1
1
0
9
/
J
B
H
I
.
2
0
1
5
.
2
4
2
9
5
5
6
.
[
1
7
]
P
.
R
.
B
u
v
a
n
e
sw
a
r
i
a
n
d
R
.
G
a
y
a
t
h
r
i
,
“
D
e
e
p
l
e
a
r
n
i
n
g
-
b
a
s
e
d
s
e
g
m
e
n
t
a
t
i
o
n
i
n
c
l
a
ssi
f
i
c
a
t
i
o
n
o
f
A
l
z
h
e
i
m
e
r
’
s
d
i
sea
s
e
,
”
Ara
b
i
a
n
J
o
u
rn
a
l
f
o
r
S
c
i
e
n
c
e
a
n
d
En
g
i
n
e
e
r
i
n
g
,
v
o
l
.
4
6
,
n
o
.
6
,
p
p
.
5
3
7
3
–
5
3
8
3
,
J
u
n
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
3
3
6
9
-
020
-
0
5
1
9
3
-
z.
[
1
8
]
S
.
B
.
Ç
e
l
e
b
i
a
n
d
B
.
G
.
Em
i
r
o
ğ
l
u
,
“
A
n
o
v
e
l
d
e
e
p
d
e
n
se
b
l
o
c
k
-
b
a
se
d
m
o
d
e
l
f
o
r
d
e
t
e
c
t
i
n
g
A
l
z
h
e
i
m
e
r
’
s
d
i
s
e
a
se
,
”
A
p
p
l
i
e
d
S
c
i
e
n
c
e
s
,
v
o
l
.
1
3
,
n
o
.
1
5
,
p
.
8
6
8
6
,
J
u
l
.
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
a
p
p
1
3
1
5
8
6
8
6
.
[
1
9
]
P
.
B
a
g
l
a
t
,
A
.
W
.
S
a
l
e
h
i
,
A
.
G
u
p
t
a
,
a
n
d
G
.
G
u
p
t
a
,
“
M
u
l
t
i
p
l
e
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
m
o
d
e
l
s
f
o
r
d
e
t
e
c
t
i
o
n
o
f
A
l
z
h
e
i
mer
’
s
d
i
sea
se
u
s
i
n
g
O
A
S
I
S
d
a
t
a
se
t
,
”
i
n
I
FI
P
A
d
v
a
n
c
e
s
i
n
I
n
f
o
rm
a
t
i
o
n
a
n
d
C
o
m
m
u
n
i
c
a
t
i
o
n
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
6
1
7
,
2
0
2
0
,
p
p
.
6
1
4
–
6
2
2
.
[
2
0
]
Y
.
N
.
F
u
’
a
d
a
h
,
I
.
W
i
j
a
y
a
n
t
o
,
N
.
K
.
C
.
P
r
a
t
i
w
i
,
F
.
F
.
Ta
l
i
n
i
n
g
si
h
,
S
.
R
i
z
a
l
,
a
n
d
M
.
A
.
P
r
a
m
u
d
i
t
o
,
“
A
u
t
o
ma
t
e
d
c
l
a
ss
i
f
i
c
a
t
i
o
n
o
f
A
l
z
h
e
i
m
e
r
’
s
d
i
s
e
a
s
e
b
a
s
e
d
o
n
M
R
I
i
mag
e
p
r
o
c
e
ss
i
n
g
u
s
i
n
g
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
(
C
N
N
)
w
i
t
h
A
l
e
x
N
e
t
a
r
c
h
i
t
e
c
t
u
r
e
,
”
J
o
u
rn
a
l
o
f
Ph
y
si
c
s:
C
o
n
f
e
re
n
c
e
S
e
ri
e
s
,
v
o
l
.
1
8
4
4
,
n
o
.
1
,
p
.
1
2
0
2
0
,
M
a
r
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
8
8
/
1
7
4
2
-
6
5
9
6
/
1
8
4
4
/
1
/
0
1
2
0
2
0
.
[
2
1
]
S
.
M
u
r
u
g
a
n
e
t
a
l
.
,
“
D
E
M
N
ET
:
A
d
e
e
p
l
e
a
r
n
i
n
g
mo
d
e
l
f
o
r
e
a
r
l
y
d
i
a
g
n
o
si
s o
f
A
l
z
h
e
i
m
e
r
d
i
se
a
ses
a
n
d
d
e
m
e
n
t
i
a
f
r
o
m M
R
i
ma
g
e
s
,
”
I
EEE
A
c
c
e
ss
,
v
o
l
.
9
,
p
p
.
9
0
3
1
9
–
9
0
3
2
9
,
2
0
2
1
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
2
1
.
3
0
9
0
4
7
4
.
[
2
2
]
V
.
P
.
S
.
R
a
l
l
a
b
a
n
d
i
,
K
.
T
u
l
p
u
l
e
,
a
n
d
M
.
G
a
t
t
u
,
“
A
u
t
o
m
a
t
i
c
c
l
a
ss
i
f
i
c
a
t
i
o
n
o
f
c
o
g
n
i
t
i
v
e
l
y
n
o
r
ma
l
,
mi
l
d
c
o
g
n
i
t
i
v
e
i
m
p
a
i
r
me
n
t
a
n
d
A
l
z
h
e
i
m
e
r
’
s
d
i
se
a
se
u
s
i
n
g
st
r
u
c
t
u
r
a
l
M
R
I
a
n
a
l
y
si
s,”
I
n
f
o
rm
a
t
i
c
s
i
n
M
e
d
i
c
i
n
e
U
n
l
o
c
k
e
d
,
v
o
l
.
1
8
,
p
.
1
0
0
3
0
5
,
2
0
2
0
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
i
m
u
.
2
0
2
0
.
1
0
0
3
0
5
.
[
2
3
]
S
.
S
.
B
a
m
b
e
r
a
n
d
T.
V
i
s
h
v
a
k
a
r
ma,
“
M
e
d
i
c
a
l
i
ma
g
e
c
l
a
ssi
f
i
c
a
t
i
o
n
f
o
r
A
l
z
h
e
i
mer’
s
u
si
n
g
a
d
e
e
p
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
,
”
J
o
u
r
n
a
l
o
f
En
g
i
n
e
e
ri
n
g
a
n
d
A
p
p
l
i
e
d
S
c
i
e
n
c
e
,
v
o
l
.
7
0
,
n
o
.
1
,
M
a
y
2
0
2
3
,
d
o
i
:
1
0
.
1
1
8
6
/
s
4
4
1
4
7
-
0
2
3
-
0
0
2
1
1
-
x.
[
2
4
]
F
.
M
.
J
.
M
.
S
h
a
mr
a
t
e
t
a
l
.
,
“
A
l
z
h
e
i
merN
e
t
:
A
n
e
f
f
e
c
t
i
v
e
d
e
e
p
l
e
a
r
n
i
n
g
b
a
s
e
d
p
r
o
p
o
s
i
t
i
o
n
f
o
r
A
l
z
h
e
i
mer
’
s
d
i
se
a
se
s
t
a
g
e
s
c
l
a
ss
i
f
i
c
a
t
i
o
n
f
r
o
m
f
u
n
c
t
i
o
n
a
l
b
r
a
i
n
c
h
a
n
g
e
s
i
n
m
a
g
n
e
t
i
c
r
e
s
o
n
a
n
c
e
i
ma
g
e
s
,
”
I
EEE
Ac
c
e
ss
,
v
o
l
.
1
1
,
p
p
.
1
6
3
7
6
–
1
6
3
9
5
,
2
0
2
3
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
2
3
.
3
2
4
4
9
5
2
.
[
2
5
]
S
.
B
.
Ç
e
l
e
b
i
a
n
d
B
.
G
.
E
mi
r
o
ğ
l
u
,
“
L
e
v
e
r
a
g
i
n
g
d
e
e
p
l
e
a
r
n
i
n
g
f
o
r
e
n
h
a
n
c
e
d
d
e
t
e
c
t
i
o
n
o
f
A
l
z
h
e
i
m
e
r
’
s d
i
s
e
a
s
e
t
h
r
o
u
g
h
m
o
r
p
h
o
me
t
r
i
c
a
n
a
l
y
si
s
o
f
b
r
a
i
n
i
m
a
g
e
s,
”
T
r
a
i
t
e
m
e
n
t
d
u
S
i
g
n
a
l
,
v
o
l
.
4
0
,
n
o
.
4
,
p
p
.
1
3
5
5
–
1
3
6
5
,
A
u
g
.
2
0
2
3
,
d
o
i
:
1
0
.
1
8
2
8
0
/
t
s.
4
0
0
4
0
5
.
[
2
6
]
M
.
A
r
a
b
i
a
t
e
t
a
l
.
,
“
En
h
a
n
c
e
d
a
c
c
u
r
a
c
y
o
f
d
e
e
p
l
e
a
r
n
i
n
g
me
t
h
o
d
f
o
r
f
r
u
i
t
i
mag
e
s
c
l
a
ss
i
f
i
c
a
t
i
o
n
,
”
i
n
2
0
2
4
2
5
t
h
I
n
t
e
rn
a
t
i
o
n
a
l
Ara
b
C
o
n
f
e
re
n
c
e
o
n
I
n
f
o
rm
a
t
i
o
n
T
e
c
h
n
o
l
o
g
y
(
A
C
I
T
)
,
D
e
c
.
2
0
2
4
,
p
p
.
1
–
9
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
I
T6
2
8
0
5
.
2
0
2
4
.
1
0
8
7
7
2
3
1
.
[
2
7
]
M
.
I
q
t
a
i
t
,
J.
A
b
a
b
n
e
h
,
M
.
R
a
sm
i
,
A
.
A
b
u
-
Jas
sar,
a
n
d
S
.
A
b
u
o
w
a
i
d
a
,
“
E
n
h
a
n
c
i
n
g
f
a
c
i
a
l
f
e
a
t
u
r
e
d
e
t
e
c
t
i
o
n
:
h
y
b
r
i
d
a
c
t
i
v
e
s
h
a
p
e
a
n
d
a
c
t
i
v
e
a
p
p
e
a
r
a
n
c
e
m
o
d
e
l
(
H
A
S
A
A
M
)
,
”
I
EE
E
A
c
c
e
ss
,
v
o
l
.
1
2
,
p
p
.
1
8
9
5
9
0
–
1
8
9
6
1
0
,
2
0
2
4
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
2
4
.
3
5
0
5
5
3
6
.
[
2
8
]
S
.
H
.
K
h
a
n
,
A
.
S
o
h
a
i
l
,
M
.
M
.
Za
f
a
r
,
a
n
d
A
.
K
h
a
n
,
“
C
o
r
o
n
a
v
i
r
u
s
d
i
sea
se
a
n
a
l
y
si
s
u
si
n
g
c
h
e
s
t
X
-
r
a
y
i
ma
g
e
s
a
n
d
a
n
o
v
e
l
d
e
e
p
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
,
”
P
h
o
t
o
d
i
a
g
n
o
si
s
a
n
d
P
h
o
t
o
d
y
n
a
m
i
c
T
h
e
r
a
p
y
,
v
o
l
.
3
5
,
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
p
d
p
d
t
.
2
0
2
1
.
1
0
2
4
7
3
.
[
2
9
]
H
.
P
a
r
mar,
B
.
N
u
t
t
e
r
,
R
.
L
o
n
g
,
S
.
A
n
t
a
n
i
,
a
n
d
S
.
M
i
t
r
a
,
“
S
p
a
t
i
o
t
e
m
p
o
r
a
l
f
e
a
t
u
r
e
e
x
t
r
a
c
t
i
o
n
a
n
d
c
l
a
ss
i
f
i
c
a
t
i
o
n
o
f
A
l
z
h
e
i
mer’
s
d
i
s
e
a
se
u
s
i
n
g
d
e
e
p
l
e
a
r
n
i
n
g
3
D
-
C
N
N
f
o
r
f
M
R
I
d
a
t
a
,
”
J
o
u
r
n
a
l
o
f
Me
d
i
c
a
l
I
m
a
g
i
n
g
,
v
o
l
.
7
,
n
o
.
0
5
,
O
c
t
.
2
0
2
0
,
d
o
i
:
1
0
.
1
1
1
7
/
1
.
j
m
i
.
7
.
5
.
0
5
6
0
0
1
.
B
I
O
G
RAP
H
I
E
S
OF
A
UT
H
O
R
S
S
u
h
a
il
a
Abu
o
wa
i
d
a
re
c
e
iv
e
d
th
e
B.
S
c
.
d
e
g
re
e
s
in
c
o
m
p
u
ter
in
f
o
rm
a
ti
o
n
sy
ste
m
,
M
.
S
c
.
d
e
g
re
e
s
in
c
o
m
p
u
ter
sc
ien
c
e
fro
m
Al
A
l
-
Ba
y
t
Un
iv
e
rsity
,
Jo
rd
a
n
,
in
2
0
1
2
a
n
d
2
0
1
5
,
re
sp
e
c
ti
v
e
ly
,
a
n
d
t
h
e
P
h
.
D.
d
e
g
re
e
in
c
o
m
p
u
ter
sc
ien
c
e
fr
o
m
Un
iv
e
rsiti
S
a
in
s
M
a
lay
sia
,
M
a
lay
sia
,
in
2
0
2
3
.
Dr.
A
b
u
o
wa
id
a
is
c
u
rre
n
tl
y
w
o
rk
i
n
g
a
s
a
n
a
ss
istan
t
p
ro
fe
ss
o
r
i
n
th
e
Co
m
p
u
ter
S
c
ien
c
e
De
p
a
rtme
n
t,
a
t
Zarq
a
Un
i
v
e
rsity
,
Jo
rd
a
n
.
He
r
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
d
e
e
p
lea
rn
in
g
,
d
e
p
t
h
e
stim
a
ti
o
n
,
p
o
in
t
c
lo
u
d
,
a
n
d
c
o
m
p
u
ter
v
isi
o
n
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
su
h
il
a
o
wi
d
a
@g
m
a
il
.
c
o
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
.
3
,
J
u
n
e
20
25
:
3
5
1
9
-
3
5
2
6
3526
Za
id
Mu
sta
f
a
re
c
e
iv
e
d
h
is
P
h
.
D.
d
e
g
re
e
in
c
o
m
p
u
ter
in
f
o
rm
a
ti
o
n
s
y
ste
m
s
a
n
d
c
o
m
p
u
ter
g
ra
p
h
ics
fro
m
S
a
n
ti
a
g
o
d
e
C
o
m
p
o
ste
la
U
n
iv
e
rsit
y
-
S
p
a
in
(
2
0
1
4
–
2
0
1
9
).
P
re
v
i
o
u
sl
y
M
.
S
c
.
i
n
c
o
m
p
u
ter
in
f
o
rm
a
ti
o
n
s
y
ste
m
s
(2
0
1
2
)
fro
m
M
id
d
le
Eas
t
Un
i
v
e
rsity
,
a
n
d
a
B.
S
c
.
i
n
so
ftwa
re
e
n
g
i
n
e
e
rin
g
(
2
0
0
7
)
fro
m
Al
-
Ba
lq
a
A
p
p
li
e
d
U
n
iv
e
rsit
y
,
Al
-
S
a
lt
,
Ha
sh
e
m
it
e
Ki
n
g
d
o
m
o
f
Jo
rd
a
n
.
Cu
rre
n
t
ly
,
h
e
se
rv
e
s a
s
a
n
a
ss
istan
t
p
ro
fe
ss
o
r
a
t
Al
-
Ba
lq
a
Ap
p
li
e
d
Un
iv
e
rsity
,
P
rin
c
e
Ab
d
u
l
lah
b
in
G
h
a
z
i
F
a
c
u
lt
y
o
f
I
n
fo
rm
a
ti
o
n
a
n
d
Co
m
m
u
n
ica
ti
o
n
Tec
h
n
o
l
o
g
y
,
Jo
rd
a
n
(
2
0
1
9
–
p
re
se
n
t).
He
is
a
lso
h
e
a
d
o
f
th
e
Co
m
p
u
ter
In
f
o
rm
a
ti
o
n
S
y
ste
m
s
D
e
p
a
rtme
n
t
(2
0
2
2
–
p
re
se
n
t).
His
re
se
a
rc
h
in
tere
sts
i
n
c
lu
d
e
m
a
c
h
in
e
lea
rn
in
g
,
h
u
m
a
n
-
c
o
m
p
u
ter
i
n
tera
c
ti
o
n
,
d
e
v
e
lo
p
i
n
g
g
e
o
g
ra
p
h
ic
in
f
o
rm
a
ti
o
n
s
y
ste
m
(G
IS
)
to
o
ls,
v
irt
u
a
l
re
a
li
t
y
,
a
u
g
m
e
n
ted
re
a
li
ty
,
t
h
e
in
tern
e
t
o
f
th
in
g
s (Io
T),
a
n
d
sm
a
rt
c
it
ies
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
z
lam
i@b
a
u
.
e
d
u
.
jo
.
Ahm
a
d
Abu
r
o
m
m
a
n
is
c
u
rr
e
n
tl
y
a
n
a
ss
istan
t
p
r
o
fe
ss
o
r
o
f
a
rt
ifi
c
ial
in
tel
li
g
e
n
c
e
in
th
e
De
p
a
rtme
n
t
o
f
Da
ta
S
c
ien
c
e
a
n
d
Artifi
c
ial
In
telli
g
e
n
c
e
i
n
t
h
e
Zar
q
a
Un
iv
e
rsity
.
He
re
c
e
iv
e
d
a
B.
S
c
.
d
e
g
re
e
in
i
n
fo
r
m
a
ti
o
n
tec
h
n
o
lo
g
y
fro
m
Al
-
Ba
l
q
a
Ap
p
li
e
d
U
n
iv
e
rsit
y
(BAU
),
Jo
rd
a
n
,
i
n
2
0
0
5
,
a
n
d
a
n
M
.
S
c
.
d
e
g
re
e
in
in
f
o
rm
a
ti
o
n
tec
h
n
o
lo
g
y
(e
-
se
rv
ice
s)
fro
m
G
riff
it
h
Un
iv
e
rsity
,
Au
stra
li
a
,
i
n
2
0
0
9
.
H
e
g
o
t
a
P
h
.
D.
d
e
g
re
e
in
i
n
telli
g
e
n
c
e
sy
ste
m
s
a
t
th
e
Re
se
a
rc
h
Ce
n
ter
in
I
n
telli
g
e
n
t
Tec
h
n
o
l
o
g
ies
(CiT
IUS),
Un
i
v
e
rsity
o
f
S
a
n
ti
a
g
o
d
e
C
o
m
p
o
ste
la.
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
m
a
c
h
in
e
lea
rn
in
g
,
d
e
e
p
lea
rn
in
g
.
H
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
h
m
a
d
.
a
b
u
r
o
m
a
n
@g
m
a
il
.
c
o
m
.
Na
wa
f
Alshd
a
ifa
t
re
c
e
iv
e
d
th
e
B.
S
c
.
in
c
o
m
p
u
ter
sc
ien
c
e
fro
m
Al
a
l
-
Ba
y
t
Un
iv
e
rsity
,
Jo
rd
a
n
,
M
.
S
c
.
d
e
g
re
e
o
f
c
o
m
p
u
ter
sc
ien
c
e
fro
m
Un
iv
e
rsity
o
f
Jo
rd
a
n
,
Jo
rd
a
n
,
in
2
0
0
2
a
n
d
2
0
1
1
,
re
sp
e
c
ti
v
e
l
y
a
n
d
th
e
P
h
.
D.
d
e
g
re
e
in
c
o
m
p
u
ter
sc
i
e
n
c
e
s
fro
m
Un
iv
e
rsiti
S
a
in
s
M
a
lay
sia
,
M
a
lay
sia
,
in
2
0
2
3
.
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
d
e
e
p
lea
rn
in
g
a
n
d
trac
k
i
n
g
o
b
jec
ts.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
n
a
l
sh
d
a
ifat@a
su
.
e
d
u
.
jo
.
Mu
sa
b
Iqt
a
it
is
a
n
a
ss
istan
t
p
r
o
fe
ss
o
r
a
t
th
e
F
a
c
u
l
ty
o
f
In
fo
rm
a
ti
o
n
a
n
d
Tec
h
n
o
l
o
g
y
,
Zarq
a
U
n
iv
e
rsit
y
,
J
o
rd
a
n
.
He
h
o
l
d
s
a
P
h
.
D.
d
e
g
re
e
in
c
o
m
p
u
ter
sc
ien
c
e
wit
h
a
sp
e
c
ializa
ti
o
n
i
n
a
rti
ficia
l
in
telli
g
e
n
c
e
fro
m
t
h
e
F
a
c
u
lt
y
o
f
I
n
fo
rm
a
ti
c
s
a
n
d
Co
m
p
u
ti
n
g
a
t
Un
iv
e
rsiti
S
u
lt
a
n
Zain
a
l
Ab
i
d
in
in
Tere
n
g
g
a
n
u
,
M
a
lay
sia
.
He
is
a
re
c
ip
ien
t
o
f
d
iffere
n
t
n
a
ti
o
n
a
l
a
n
d
i
n
tern
a
ti
o
n
a
l
a
wa
r
d
s,
su
c
h
a
s
th
e
IP
RC
2
0
1
9
o
v
e
ra
ll
b
e
st
p
a
p
e
r
a
wa
rd
.
His
re
se
a
rc
h
in
tere
sts in
c
lu
d
e
ima
g
e
/s
ig
n
a
l
p
ro
c
e
ss
in
g
,
b
io
m
e
tri
c
s,
a
n
d
p
a
tt
e
rn
re
c
o
g
n
it
i
o
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.