I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
pu
t
er
Science
Vo
l.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
,
p
p
.
1
6
13
~
1
6
19
I
SS
N:
2
502
-
4
7
52
,
DOI
: 1
0
.
1
1
5
9
1
/ijee
cs
.v
3
6
.
i
3
.
pp
1
6
13
-
1
6
19
1613
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ee
cs
.
ia
esco
r
e.
co
m
Dev
elo
ped impro
v
ed lion o
ptimiza
t
io
n f
o
r breas
t
can
cer
cla
ss
ificatio
n usin
g
histopa
tholo
g
y
ima
g
es
P
a
t
t
a
n M
.
D
.
Ali K
ha
n,
Xa
v
i
er
Arput
ha
Ra
t
hin
a
D
e
p
a
r
t
me
n
t
o
f
C
S
E,
B
.
S
A
b
d
u
r
R
a
h
m
a
n
C
r
e
s
c
e
n
t
I
n
st
i
t
u
t
e
o
f
S
c
i
e
n
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
,
C
h
e
n
n
a
i
,
I
n
d
i
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
May
15
,
2
0
2
4
R
ev
is
ed
Au
g
19
,
202
4
Acc
ep
ted
A
ug
26
,
2
0
2
4
Bre
a
st
c
a
n
c
e
r,
a
p
re
v
a
len
t
k
in
d
o
f
c
a
n
c
e
r,
is
a
m
a
jo
r
h
e
a
lt
h
p
ro
b
le
m
a
m
o
n
g
wo
m
e
n
.
Re
se
a
rc
h
e
rs
re
c
e
n
tl
y
a
c
h
iev
e
d
c
a
teg
o
riza
ti
o
n
e
ffe
c
ti
v
e
n
e
ss
o
f
b
re
a
st
c
a
n
c
e
r
(
BC
)
d
e
tec
ti
o
n
in
h
isto
p
a
th
o
lo
g
y
p
ictu
re
d
a
tab
a
se
u
sin
g
c
o
n
v
o
lu
ti
o
n
a
l
n
e
u
ra
l
n
e
two
r
k
s
(CNN
s)
o
f
m
e
d
ica
l
ima
g
e
p
ro
c
e
ss
in
g
.
Alth
o
u
g
h
CNN
m
e
th
o
d
p
a
ra
m
e
ter
se
tt
in
g
s
we
re
c
o
m
p
lex
,
e
m
p
lo
y
i
n
g
b
re
a
st
c
a
n
c
e
r
h
isto
p
a
t
h
o
l
o
g
ica
l
d
a
tab
a
s
e
(BCHD
)
d
a
ta
fo
r
c
a
teg
o
riz
a
ti
o
n
wa
s
v
a
lu
e
d
a
s
e
x
p
e
n
si
v
e
.
Th
is
re
se
a
rc
h
u
se
d
u
n
if
o
rm
e
x
p
e
rime
n
tal
d
e
si
g
n
(UED)
to
s
o
lv
e
th
e
se
issu
e
s
a
n
d
imp
r
o
v
e
d
l
io
n
o
p
t
imiz
a
ti
o
n
(IL
O)
b
re
a
st
c
a
n
c
e
r
h
isto
p
a
th
o
lo
g
y
ima
g
e
c
a
teg
o
riza
ti
o
n
.
To
o
p
ti
m
ize
th
e
v
a
riab
les
a
t
UED
-
ILO,
a
re
g
re
ss
io
n
m
e
th
o
d
wa
s
e
m
p
l
o
y
e
d
.
Ac
c
o
rd
i
n
g
t
o
t
h
e
e
x
p
e
rime
n
ta
l
d
a
ta,
th
e
p
ro
p
o
se
d
a
p
p
r
o
a
c
h
o
f
UED
-
IL
O
(
u
n
ifo
rm
e
x
p
e
rime
n
tal
d
e
si
g
n
b
a
se
d
imp
ro
v
e
d
li
o
n
o
p
ti
m
iza
ti
o
n
)
v
a
ri
a
b
le
o
p
ti
m
iza
ti
o
n
p
r
o
v
i
d
e
d
a
c
a
teg
o
riza
ti
o
n
a
c
c
u
ra
c
y
ra
te
o
f
8
4
.
4
1
%
.
F
i
n
a
ll
y
,
th
e
p
r
o
p
o
se
d
a
p
p
r
o
a
c
h
c
a
n
e
ffe
c
ti
v
e
ly
in
c
re
a
se
c
las
sifica
ti
o
n
a
c
c
u
ra
c
y
,
with
re
su
lt
s
t
h
a
t
o
u
t
p
e
rfo
rm
o
t
h
e
rs
o
f
a
n
e
q
u
iv
a
len
t
n
a
t
u
re
.
K
ey
w
o
r
d
s
:
B
r
ea
s
t c
an
ce
r
C
o
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
Dee
p
lear
n
in
g
His
to
p
ath
o
lo
g
ical
d
atab
ase
I
m
p
r
o
v
ed
lio
n
o
p
tim
izatio
n
Un
if
o
r
m
ex
p
er
im
en
tal
d
esig
n
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
:
Pattan
M
.
D
.
Ali K
h
an
Dep
ar
tm
en
t o
f
C
SE,
B
.
S Ab
d
u
r
R
ah
m
an
C
r
escen
t I
n
s
titu
te
o
f
Scien
ce
an
d
T
ec
h
n
o
lo
g
y
C
h
en
n
ai,
I
n
d
ia
E
m
ail:
alik
_
1
2
@
r
ed
if
f
m
ail.
c
o
m
1.
I
NT
RO
D
UCT
I
O
N
B
r
ea
s
t
c
an
ce
r
(
B
C
)
i
s
th
e
m
o
s
t
f
r
eq
u
e
n
t
m
alig
n
a
n
cy
in
wo
m
en
wo
r
ld
wid
e.
I
n
T
aiwa
n
(
p
o
p
u
latio
n
2
3
m
illi
o
n
)
,
1
in
1
2
0
wo
m
en
was
r
ec
o
g
n
ized
with
B
C
ea
ch
y
ea
r
,
an
d
th
e
p
r
e
v
alen
ce
o
f
B
C
was
r
is
in
g
[
1
]
.
His
to
p
ath
o
lo
g
ical
im
ag
e
ca
teg
o
r
izatio
n
ac
cu
r
ac
y
is
cr
itical
f
o
r
ea
r
ly
B
C
d
etec
ti
o
n
.
B
C
d
iag
n
o
s
is
r
elies
o
n
h
is
to
p
ath
o
lo
g
ical
im
a
g
in
g
m
eth
o
d
s
s
u
ch
as
u
ltra
s
o
u
n
d
,
p
o
s
itro
n
em
is
s
io
n
to
m
o
g
r
a
p
h
y
(
PET
)
,
s
u
r
g
ical
in
cisi
o
n
m
am
m
o
g
r
a
p
h
y
,
th
er
m
o
g
r
ap
h
y
,
a
n
d
m
ag
n
etic
r
eso
n
an
ce
im
ag
in
g
(
MRI)
[2
]
,
[
3
].
D
iag
n
o
s
in
g
B
C
u
s
in
g
h
is
to
p
ath
o
lo
g
ical
p
ictu
r
es
was
ch
allen
g
in
g
in
th
e
ea
r
l
y
s
tag
es
s
in
ce
th
ese
p
ictu
r
es
ca
n
tr
an
s
m
it
war
n
in
g
s
ig
n
als
an
d
s
y
m
p
to
m
s
[
4
]
.
A
s
a
r
esu
lt,
a
v
a
r
iety
o
f
c
o
m
p
u
ter
-
ass
is
ted
s
o
lu
tio
n
s
was
cr
e
ated
to
ad
d
r
ess
th
e
s
h
o
r
tco
m
in
g
s
o
f
h
is
to
lo
g
ical
p
ictu
r
e
ev
alu
atio
n
[
5
]
.
T
h
e
r
ese
ar
ch
team
was
th
e
v
is
u
al
g
eo
m
etr
y
g
r
o
u
p
(
VGG)
o
r
Go
o
g
le
o
f
r
ec
en
tly
ap
p
lied
ad
v
an
ce
d
en
g
in
ee
r
in
g
m
eth
o
d
s
to
b
u
ild
th
e
VGG
-
1
6
,
r
esid
u
al
n
etwo
r
k
(
R
esNet)
,
o
r
g
en
er
al
n
u
r
s
in
g
an
d
m
id
wif
er
y
(
GNM
)
[
6
]
.
D
ee
p
l
ea
r
n
in
g
(
DL
)
m
o
d
els
b
ased
o
n
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
(
C
NN
)
ar
e
am
o
n
g
th
e
tech
n
ical
m
eth
o
d
s
u
tili
ze
d
to
en
h
an
ce
th
e
ef
f
icien
cy
o
f
b
r
ea
s
t
ca
n
ce
r
p
r
ed
ictio
n
[
7
]
-
[
9
]
.
T
h
is
was
ac
co
m
p
lis
h
ed
b
y
h
y
p
e
r
v
ar
iab
le
o
p
tim
izatio
n
.
T
o
in
c
r
ea
s
e
th
e
p
r
ec
is
io
n
attain
ed
to
p
r
ev
io
u
s
r
esear
ch
em
p
lo
y
in
g
B
r
ea
k
His
o
f
p
ictu
r
e
ca
teg
o
r
izat
io
n
,
th
is
r
esear
ch
em
p
lo
y
s
DL
n
etwo
r
k
s
as
a
C
NN
with
v
ar
iab
le
o
p
tim
izatio
n
.
DL
n
etwo
r
k
s
g
e
n
er
ate
a
m
az
in
g
o
u
tco
m
es
to
p
ictu
r
e
e
v
a
lu
atio
n
ap
p
licatio
n
s
in
m
ed
icin
e
[
1
0
]
.
T
h
e
L
eNe
t
-
5
s
y
s
tem
is
a
p
o
wer
f
u
l
n
etwo
r
k
o
f
C
NN
ap
p
licatio
n
s
th
at
h
as
a
h
ig
h
r
ec
o
g
n
itio
n
r
ate
.
L
eNe
t
-
5
(
R
MSp
r
o
p
-
r
o
o
t
m
ea
n
s
q
u
ar
ed
p
r
o
p
ag
atio
n
)
[
1
1
]
,
L
eNe
t
-
5
(
SGDM
-
s
to
ch
asti
c
g
r
ad
ien
t
d
escen
t
with
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
6
13
-
1
6
19
1614
m
o
m
en
tu
m
)
[
1
2
]
,
L
eNe
t
-
5
(
A
d
am
-
an
aly
s
is
d
ata
m
o
d
el
)
[
1
3
]
,
an
d
s
in
g
le
-
lay
er
C
NN
[
1
4
]
,
an
d
r
an
d
o
m
f
o
r
est
(
RF
)
class
if
ier
+
P
FTAS
[
1
5
]
ar
e
s
o
m
e
o
th
er
DL
n
etwo
r
k
s
.
T
o
s
o
lv
e
p
r
ac
tical
is
s
u
es,
C
NN
tr
ain
in
g
n
ec
ess
itates
ex
ten
s
iv
e
ca
lcu
lat
io
n
with
h
ig
h
s
am
p
le
s
izes
an
d
v
ar
iab
le
s
ettin
g
s
[
1
6
]
.
T
h
is
r
ed
u
ce
s
th
e
n
u
m
b
er
o
f
n
etwo
r
k
s
ca
lcu
latin
g
s
am
p
les
wh
ile
m
ee
tin
g
th
e
es
s
e
n
tial
p
ar
am
eter
v
alu
es
f
o
r
C
NN
ap
p
licatio
n
s
.
E
x
p
er
im
en
tatio
n
is
u
s
ed
to
id
en
tify
th
e
m
o
s
t
ap
p
r
o
p
r
iate
C
NN
m
o
d
el
[
1
7
]
,
[
1
8
]
.
L
atch
o
u
m
i
et
a
l.
[
1
9
]
a
ls
o
em
p
lo
y
ed
a
u
n
if
o
r
m
ex
p
e
r
im
en
tal
d
esig
n
(
UE
D
)
to
d
et
er
m
in
e
th
e
n
etwo
r
k
'
s
v
ar
iab
l
es
wh
ich
en
h
an
ce
d
ev
alu
atio
n
.
T
h
e
C
NN
v
ar
iab
les
u
s
ed
f
o
r
B
C
h
is
to
p
ath
o
lo
g
y
im
ag
e
ca
teg
o
r
izatio
n
,
o
n
th
e
o
th
er
h
an
d
,
ar
e
o
p
tim
ized
u
s
in
g
a
UE
D
in
th
is
s
tu
d
y
[
2
0
]
.
UE
D
an
ap
p
r
o
ac
h
th
at
was
s
t
atis
tical
ex
p
er
im
en
tal
d
esig
n
m
ath
em
atica
l
s
tatis
t
ics,
an
d
p
r
o
b
ab
ilit
y
th
eo
r
y
,
[
2
1
]
was
u
tili
ze
d
to
o
p
tim
ize
C
NN
v
ar
iab
les
an
d
r
ed
u
ce
ex
p
e
r
im
en
t
c
o
m
p
u
tatio
n
ti
m
e.
UE
D
co
u
ld
b
e
u
tili
ze
d
to
id
en
tify
ap
p
r
o
p
r
iate
s
am
p
le
g
r
o
u
p
s
an
d
d
is
tr
ib
u
t
e
all
av
ailab
le
ex
p
er
im
en
tal
v
ar
iab
les
o
b
tain
ed
in
s
o
m
e
s
tu
d
ies
test
s
[
2
2
]
.
Acc
o
r
d
in
g
to
a
co
llectio
n
o
f
UE
D
t
ab
les,
th
e
n
u
m
b
er
o
f
lay
e
r
s
eq
u
als
th
e
n
u
m
b
er
o
f
test
r
u
n
s
[
2
3
]
.
Fu
r
th
e
r
m
o
r
e
,
to
o
b
tain
ap
p
r
o
p
r
iately
p
r
e
cise
p
r
o
jectio
n
s
,
UE
D
ex
am
in
es
th
e
v
ar
iab
les
im
p
ac
tin
g
th
e
f
i
n
d
in
g
s
in
a
r
es
tr
icted
n
u
m
b
e
r
o
f
ex
p
e
r
im
en
ts
[
2
4
]
.
T
h
e
UE
D
ap
p
r
o
ac
h
is
em
p
l
o
y
ed
in
th
is
s
tu
d
y
t
o
o
p
tim
ize
th
e
C
NN
ap
p
r
o
ac
h
p
ar
am
et
er
s
to
th
e
ap
p
licatio
n
o
f
B
C
h
is
to
p
ath
o
l
o
g
y
p
ictu
r
e
ca
teg
o
r
izatio
n
[
2
5
]
.
T
h
e
c
u
r
r
e
n
t
wo
r
k
cr
ea
ted
a
C
NN
d
ep
en
d
in
g
o
n
UE
D
to
o
v
er
co
m
e
th
e
d
if
f
icu
lt
v
ar
iab
le
s
etu
p
is
s
u
e
to
im
p
r
o
v
e
class
if
icatio
n
ac
cu
r
ac
y
.
As
an
o
u
tco
m
e,
th
e
k
ey
co
n
tr
ib
u
tio
n
o
f
th
is
r
e
s
ea
r
ch
was
to
ap
p
ly
th
e
UE
D
tech
n
iq
u
e
to
f
in
d
th
e
b
est
C
NN
ar
ch
itectu
r
al
v
ar
iab
le
co
m
b
in
atio
n
f
o
r
d
o
in
g
th
e
f
ewe
s
t
n
u
m
b
er
o
f
test
s
an
d
t
ak
in
g
th
e
least
am
o
u
n
t
o
f
ti
m
e.
T
h
e
r
eg
r
ess
io
n
ev
alu
atio
n
was
em
p
lo
y
e
d
in
th
e
UE
D
ap
p
r
o
ac
h
to
d
eter
m
in
e
o
p
tim
izatio
n
v
ar
iab
les
in
C
NN
ar
ch
itectu
r
e.
T
h
e
p
r
o
p
o
s
ed
C
NN
-
b
ased
u
n
if
o
r
m
ex
p
er
im
en
tal
d
esig
n
-
b
ased
im
p
r
o
v
ed
lio
n
o
p
tim
izatio
n
(
UE
D
-
ILO
)
v
ar
iab
le
o
p
tim
izatio
n
m
eth
o
d
s
u
r
p
ass
es
cu
r
r
en
t
m
eth
o
d
s
with
h
ig
h
ac
cu
r
ac
y
,
m
ak
i
n
g
it
m
o
r
e
u
s
ef
u
l
in
clin
ical
d
iag
n
o
s
tics
.
2.
P
RO
P
O
SE
D
SYS
T
E
M
T
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
c
o
m
b
in
es
a
C
NN
with
u
n
s
u
p
er
v
is
ed
ev
o
lu
tio
n
ar
y
DL
u
s
in
g
in
d
ir
ec
t
en
co
d
in
g
with
lo
ca
l
o
p
tim
izatio
n
(
UE
D
-
I
L
O)
.
T
h
is
f
u
s
io
n
a
im
s
to
en
h
an
ce
th
e
class
if
icati
o
n
p
er
f
o
r
m
an
ce
o
f
th
e
m
o
d
el
b
y
lev
er
ag
i
n
g
th
e
s
tr
en
g
th
s
o
f
b
o
th
C
NNs
an
d
ev
o
lu
tio
n
ar
y
alg
o
r
ith
m
s
.
Fig
u
r
e
1
illu
s
tr
ates
th
e
ar
ch
itectu
r
al
lay
o
u
t
o
f
th
e
p
r
o
p
o
s
ed
tech
n
i
q
u
e,
s
h
o
wca
s
in
g
h
o
w
th
e
C
NN
an
d
UE
D
-
I
L
O
co
m
p
o
n
e
n
ts
ar
e
in
teg
r
ated
to
ac
h
iev
e
im
p
r
o
v
e
d
class
if
icatio
n
ac
cu
r
ac
y
.
Fig
u
r
e
1
.
T
h
e
p
r
o
p
o
s
ed
a
p
p
r
o
ac
h
f
o
r
h
is
to
p
ath
o
lo
g
ical
im
ag
e
ca
teg
o
r
izatio
n
o
f
b
r
ea
s
t c
an
c
er
2
.
1
.
M
a
t
er
ia
ls
T
h
e
ex
p
er
im
e
n
tal
p
ictu
r
es
in
th
is
in
v
esti
g
atio
n
wer
e
g
en
e
r
ated
th
r
o
u
g
h
b
r
ea
s
t
tis
s
u
e
s
a
m
p
les
an
d
B
r
ea
k
His
wer
e
g
ath
er
ed
f
r
o
m
8
2
p
eo
p
le
i
n
th
e
B
r
az
ili
an
C
y
to
p
ath
o
l
o
g
y
L
ab
o
r
at
o
r
y
an
d
p
ath
o
l
o
g
ical
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Dev
elo
p
ed
imp
r
o
ve
d
lio
n
o
p
timiz
a
tio
n
fo
r
b
r
ea
s
t c
a
n
ce
r
…
(
P
a
tta
n
M
.
D
.
A
li K
h
a
n
)
1615
an
ato
m
y
.
Mu
ltip
le
b
r
ea
s
t
tis
s
u
e
s
am
p
les
wer
e
asp
ir
ated
in
th
e
o
p
er
atin
g
r
o
o
m
f
o
r
ea
ch
p
atien
t
u
s
in
g
a
tin
y
b
io
p
s
y
n
ee
d
le.
E
ac
h
s
am
p
le
was
cr
ea
ted
in
th
e
f
o
llo
win
g
m
an
n
er
:
t
o
r
etain
t
h
e
o
r
ig
i
n
al
tis
s
u
e
s
tr
u
ctu
r
e
an
d
m
o
lecu
lar
m
a
k
eu
p
,
im
p
lan
tatio
n
in
p
ar
a
f
f
in
b
lo
ck
s
an
d
f
o
r
m
alin
f
ix
atio
n
wer
e
co
n
d
u
cte
d
f
ir
s
t.
T
h
e
p
ar
af
f
in
b
lo
ck
s
wer
e
th
en
s
liced
in
to
th
e
3
-
µm
-
th
ick
s
ec
tio
n
s
u
s
in
g
a
h
ig
h
-
po
wer
m
icr
o
wav
e
(
HPM
)
.
L
astl
y
,
th
e
d
iv
is
io
n
s
wer
e
d
is
p
lay
ed
o
n
co
ated
g
lass
s
lid
es
an
d
v
iewe
d
u
s
in
g
a
lig
h
t
m
icr
o
s
co
p
e.
B
r
ea
k
His
in
clu
d
es
7
,
9
0
9
7
0
0
×4
6
0
-
p
i
x
el
h
is
to
p
at
h
o
lo
g
ical
p
h
o
to
s
o
f
B
C
at
f
o
u
r
r
is
in
g
m
ag
n
if
icat
io
n
s
(
4
0
×
,
1
0
0
×,
2
0
0
×,
an
d
4
0
0
×)
,
with
5
,
4
2
9
an
d
2
,
4
8
0
p
ictu
r
es
o
f
ca
n
ce
r
o
u
s
an
d
b
en
ig
n
tu
m
o
r
s
,
co
r
r
esp
o
n
d
in
g
l
y
s
h
o
wn
in
Fig
u
r
e
2
,
f
u
r
th
er
Fig
u
r
e
2
(
a)
s
h
o
ws
th
e
m
ag
n
if
icatio
n
at
4
0
×
,
Fig
u
r
e
2
(
b
)
s
h
o
ws
th
e
m
ag
n
if
icatio
n
at
1
0
0
x
,
Fig
u
r
e
2
(
c)
s
h
o
ws
th
e
m
ag
n
if
icatio
n
at
2
0
0
x
,
Fig
u
r
e
2
(
d
)
s
h
o
ws
th
e
m
a
g
n
if
icatio
n
o
f
4
0
0
×
.
Alth
o
u
g
h
th
e
class
im
b
alan
ce
is
s
u
e
co
u
ld
s
k
ew
C
NN
clas
s
if
icatio
n
'
s
d
i
s
cr
im
in
ativ
e
ca
p
ab
ilit
y
;
th
is
i
s
th
e
B
r
ea
k
His
d
atab
ase
r
estrictio
n
,
an
d
it
wo
u
ld
in
clin
e
to
p
r
e
d
ict
p
i
ctu
r
es
as
m
alig
n
an
t
a
s
s
h
o
wn
in
T
ab
le
1
.
As
a
r
esu
lt,
th
e
g
ath
er
ed
d
ata
wer
e
s
ep
ar
ated
in
to
v
alid
atio
n
an
d
t
r
ain
in
g
g
r
o
u
p
s
,
with
th
e
f
ir
s
t
7
0
%
o
f
p
h
o
to
g
r
a
p
h
s
u
s
ed
to
tr
a
in
th
e
n
etwo
r
k
a
n
d
th
e
b
alan
ce
o
f
3
0
%
u
s
ed
to
v
al
id
ate.
(
a)
(
b
)
(
c)
(
d
)
Fig
u
r
e
2
.
His
to
p
ath
o
l
o
g
ical
p
i
ctu
r
es o
f
B
C
:
(
a)
4
0
×,
(
b
)
1
0
0
×,
(
c)
2
0
0
×,
a
n
d
(
d
)
4
0
0
×
m
a
g
n
if
icatio
n
T
ab
le
1
.
B
r
ea
k
His
d
atab
ase
p
i
ctu
r
e
s
eg
r
eg
atio
n
b
y
class
M
a
g
n
i
f
i
c
a
t
i
o
n
M
a
l
i
g
n
a
n
t
B
e
n
i
g
n
To
t
a
l
4
0
x
1
0
0
x
2
0
0
x
4
0
0
x
1
,
3
7
5
1
,
4
4
4
1
,
4
0
2
1
,
2
3
5
6
5
4
6
4
6
6
2
8
5
7
2
2
,
0
0
2
2
,
0
8
5
2
,
0
1
5
1
,
8
2
3
To
t
a
l
i
m
a
g
e
s
5
,
4
5
6
2
,
5
0
0
7
,
9
2
5
2
.
2
.
Alg
o
rit
hm
s
f
o
r
UE
D
-
ILO
T
h
is
m
eth
o
d
is
u
s
ed
to
d
eter
m
in
e
th
e
b
est
.
T
h
e
b
u
b
b
le
-
n
e
t
s
er
v
in
g
m
et
h
o
d
was
u
s
ed
to
h
u
n
t
th
e
an
im
al.
T
h
is
ap
p
r
o
ac
h
co
n
s
is
ts
o
f
two
b
asic
p
h
ases
:
s
u
r
r
o
u
n
d
in
g
th
e
p
r
e
y
an
d
en
v
elo
p
in
g
it
with
a
b
u
b
b
le
n
etwo
r
k
.
Sear
c
h
in
g
f
o
r
h
u
m
p
b
ac
k
lio
n
f
o
o
d
was
d
o
n
e
t
o
r
ea
ch
a
wo
r
ld
wid
e
m
ax
im
u
m
/m
in
im
u
m
.
T
h
e
p
r
o
ce
d
u
r
e
o
f
n
o
u
r
is
h
in
g
a
h
u
m
p
b
ac
k
lio
n
with
a
b
u
b
b
le
n
et
is
d
escr
ib
ed
in
(
1
)
an
d
(
2
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
6
13
-
1
6
19
1616
+
1
=
{
∗
−
<
0
.
5
.
c
os
(
2
)
.
1
+
∗
≥
0
.
5
(
1
)
1
=
[
∗
−
]
(
2
)
W
h
er
e
P*
-
u
p
d
ate
th
e
p
o
p
u
la
tio
n
-
b
ased
b
est
s
o
lu
tio
n
u
s
in
g
an
o
b
jectiv
e
f
u
n
ctio
n
.
E
an
d
B
ar
e
v
ec
to
r
co
ef
f
icien
t
v
ec
to
r
s
a
n
d
u
s
in
g
(
3
)
an
d
(
4
)
.
d
en
o
tes
th
e
r
an
g
e
v
ec
to
r
r
an
g
e
f
r
o
m
0
t
o
1
.
T
h
e
weig
h
ted
q
u
an
tu
m
f
itn
ess
f
u
n
ctio
n
ca
n
b
e
ca
lc
u
lated
b
y
u
s
in
g
(
5
)
.
W
h
er
e
M
matrix
d
en
o
tes
m
em
b
er
s
h
ip
m
atr
ix
s
ea
r
ch
ag
en
t,
C
c
s
ig
n
if
ies
C
C
an
d
th
e
d
ata
elem
en
ts
clu
s
ter
ed
u
s
in
g
th
e
p
r
o
p
o
s
ed
s
y
s
tem
p
r
o
v
id
e
b
etter
r
esu
lts
an
d
en
h
an
ce
th
e
f
itn
ess
v
alu
e.
Fo
r
lio
n
o
p
tim
izatio
n
(
LO
)
,
th
e
lo
g
is
tics
m
ap
p
in
g
(
L
M
)
m
eth
o
d
is
u
s
ed
an
d
d
eter
m
in
ed
b
y
u
s
in
g
(
6
)
.
Alg
o
r
ith
m
1
s
h
o
ws th
e
UE
D
-
I
L
O
a
lg
o
r
ith
m
.
=
2
.
.
−
(
3
)
=
2
.
(
4
)
(
)
=
1
(
.
)
+
1
(
5
)
+
1
=
(
1
−
)
(
6
)
Alg
o
r
ith
m
1
.
Alg
o
r
ith
m
f
o
r
UE
D
-
ILO
Input: Scanned images (MRI)
Output:
Predict the histopathology
Begin
Step 1
Transformed into the greyscale image from the MRI image
Step 2
Segment the greyscale image using FCM
Step 3
Extract the features from the segmented image
Step 4
Steps to follow UED features are for classifying histop
athology
(i) Calculate the input features
(
(
)
)
=
∑
(
)
=
1
(7)
(ii) Weighed Quantum estimation using (8)
=
(
)
−
1
(8)
(iii) RBF estimation
(
)
=
[
−
|
(
)
−
|
2
2
2
]
(9)
Step 5
ILO algorithm to calculate the optimum
value using (9)
End
Ou
r
alg
o
r
ith
m
p
r
o
v
e
d
s
u
s
ce
p
tib
le
to
h
u
m
an
b
iases
an
d
m
is
tak
es
s
in
ce
it
was
tr
ain
ed
o
n
d
at
a
th
at
h
ad
p
r
ev
io
u
s
ly
b
ee
n
lab
eled
b
y
s
k
i
lled
p
ath
o
lo
g
is
ts
.
I
n
ad
d
itio
n
t
o
an
y
an
o
m
alies o
r
in
co
n
s
is
ten
cies in
o
u
r
d
ataset,
th
e
s
ca
le
o
f
o
u
r
d
ataset
p
o
s
es
a
s
er
io
u
s
is
s
u
e
f
o
r
DL
m
o
d
els.
L
ar
g
e
am
o
u
n
ts
o
f
d
ata
h
av
e
b
ee
n
u
s
ed
ex
ten
s
iv
ely
in
th
e
lear
n
in
g
o
f
s
ev
er
al
lar
g
e
-
s
ca
le
m
o
d
els.
3.
E
XP
E
R
I
M
E
N
T
A
L
RE
SUL
T
S
T
h
e
th
r
ee
ex
p
er
im
en
ts
m
ay
h
av
e
u
s
ed
th
e
s
am
e
v
a
r
iab
le
c
o
n
f
ig
u
r
atio
n
s
,
y
et
ea
c
h
o
b
s
er
v
atio
n
was
d
o
cu
m
e
n
ted
s
ep
ar
ately
.
T
o
r
u
n
2
o
f
th
is
s
tu
d
y
,
C
NN
'
s
av
er
ag
e
class
if
icatio
n
ac
cu
r
ac
y
is
8
3
.
4
%.
Fu
r
th
er
m
o
r
e,
b
y
u
tili
zin
g
UE
D
-
I
L
O
f
o
r
r
eg
r
ess
io
n
ev
alu
atio
n
th
e
av
e
r
ag
e
ca
teg
o
r
izatio
n
ac
c
u
r
ac
y
o
f
th
e
o
p
tim
ized
s
tr
u
ctu
r
e
in
B
r
ea
k
His
r
o
s
e
to
1
.
0
1
% a
f
ter
C
NN
ar
ch
itectu
r
e
f
ac
to
r
o
p
tim
izatio
n
as sh
o
wn
in
T
ab
le
2.
T
ab
le
2.
T
h
e
o
u
tc
o
m
es o
f
a
c
o
m
p
ar
is
o
n
o
f
r
u
n
2
a
n
d
th
e
UE
D
-
ILO
R
u
n
P
a
r
a
me
t
e
r
o
f
f
a
c
t
o
r
s
P
e
r
f
o
r
ma
n
c
e
i
n
p
e
r
c
e
n
t
a
g
e
2
U
ED
-
I
LO
6
7
12
12
2
1
3
3
1
1
33
9
1
2
2
2
8
4
.
2
8
5
.
1
3
T
h
e
ev
alu
atio
n
o
f
th
e
p
r
o
p
o
s
ed
UE
D
-
I
L
O
f
ac
to
r
o
p
tim
izatio
n
m
eth
o
d
u
tili
ze
s
a
co
n
f
u
s
i
o
n
m
atr
ix
,
d
ep
icted
in
Fig
u
r
e
3
,
to
ass
ess
its
cla
s
s
if
icatio
n
p
er
f
o
r
m
a
n
ce
.
W
ith
in
th
e
2
,
3
7
3
v
er
if
ic
atio
n
p
ictu
r
es,
th
e
co
n
f
u
s
io
n
m
atr
ix
r
ev
ea
ls
1
,
9
9
7
ac
c
u
r
ate
class
if
icatio
n
s
,
d
is
tin
g
u
is
h
in
g
b
etwe
en
5
2
1
b
en
ig
n
an
d
1
,
4
7
6
ca
n
ce
r
o
u
s
ca
s
es.
Ad
d
itio
n
all
y
,
th
e
r
ec
eiv
e
r
o
p
er
atin
g
c
h
a
r
ac
ter
is
tic
(
R
OC
)
cu
r
v
e,
illu
s
tr
ated
in
T
ab
le
3
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Dev
elo
p
ed
imp
r
o
ve
d
lio
n
o
p
timiz
a
tio
n
fo
r
b
r
ea
s
t c
a
n
ce
r
…
(
P
a
tta
n
M
.
D
.
A
li K
h
a
n
)
1617
s
h
o
wca
s
es
an
ar
ea
u
n
d
e
r
th
e
c
u
r
v
e
(
AUC)
v
alu
e
o
f
0
.
8
4
2
,
in
d
icatin
g
th
e
m
o
d
el'
s
s
tr
o
n
g
d
is
cr
im
in
ativ
e
ab
ilit
y
b
etwe
en
b
en
ig
n
an
d
ca
n
ce
r
o
u
s
s
am
p
les.
Fig
u
r
e
3
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el'
s
co
n
f
u
s
io
n
a
n
d
th
e
p
r
o
p
o
s
ed
m
o
d
el'
s
R
O
C
cu
r
v
e
T
ab
le
3.
C
o
m
p
a
r
is
o
n
r
esu
lts
o
f
th
e
v
ar
i
o
u
s
m
eth
o
d
s
M
e
t
h
o
d
A
c
c
u
r
a
c
y
(
%)
M
u
l
t
i
-
l
a
y
e
r
P
F
TA
S
w
i
t
h
R
F
c
l
a
ss
i
f
i
e
r
S
G
D
M
w
i
t
h
Le
N
e
t
-
5
A
D
A
M
w
i
t
h
Le
N
e
t
-
5
R
M
S
P
r
o
p
w
i
t
h
Le
N
e
t
-
5
C
N
N
C
N
N
w
i
t
h
U
ED
-
I
LO
7
7
.
5
5
8
2
.
3
3
8
0
.
7
2
8
2
.
2
5
8
2
.
6
1
8
3
.
2
0
8
4
.
4
4
4.
CO
NCLU
SI
O
N
T
h
is
ar
ticle
p
r
o
p
o
s
es
o
p
tim
izin
g
UE
D
-
I
L
O
with
C
NN
p
ar
am
eter
s
f
o
r
d
ee
p
lear
n
i
n
g
n
etwo
r
k
s
u
tili
ze
d
in
s
tu
d
ies
o
n
b
r
ea
s
t
ca
n
ce
r
h
is
to
p
ath
o
lo
g
y
i
m
ag
e
ca
teg
o
r
izatio
n
.
T
o
o
p
tim
ize
th
e
v
ar
iab
le
co
m
b
in
atio
n
,
th
e
r
ec
o
m
m
e
n
d
ed
UE
D
m
eth
o
d
u
s
ed
a
r
e
g
r
e
s
s
io
n
ev
alu
atio
n
a
n
d
u
n
if
o
r
m
ex
p
er
im
en
t
ta
b
le
to
ch
an
g
e
th
e
C
NN
f
r
am
ew
o
r
k
.
T
esti
n
g
f
in
d
in
g
s
s
h
o
wed
th
a
t
th
e
p
r
o
p
o
s
ed
m
eth
o
d
'
s
av
er
ag
e
p
r
e
d
icted
(
with
B
r
ea
k
His
)
was
8
4
.
4
1
%,
wh
ich
is
1
.
0
1
%
g
r
ea
ter
th
a
n
t
h
e
p
r
ec
is
io
n
o
f
a
C
NN
em
p
lo
y
in
g
UE
D
-
I
L
O.
Acc
o
r
d
in
g
to
th
e
ex
p
e
r
im
en
t
al
o
u
tco
m
es,
th
e
p
r
o
p
o
s
ed
UE
D
-
I
L
O
v
ar
iab
le
o
p
tim
izatio
n
in
cr
ea
s
e
s
n
etwo
r
k
ef
f
icien
cy
an
d
o
u
tp
er
f
o
r
m
s
ex
is
tin
g
ap
p
r
o
ac
h
es.
T
h
e
b
e
n
ef
its
o
f
th
e
s
tu
d
y
in
clu
d
e
p
r
o
v
id
i
n
g
m
o
d
elin
g
u
s
er
s
with
a
lim
ited
n
u
m
b
e
r
o
f
t
ests
to
estab
lis
h
o
p
tim
al
v
a
r
iab
le
co
m
b
in
atio
n
s
o
f
C
N
N
d
esig
n
,
r
ed
u
ci
n
g
ex
p
er
im
en
tal
tim
escales,
an
d
im
p
r
o
v
in
g
class
if
icatio
n
p
r
ec
is
io
n
.
T
h
e
r
esear
ch
'
s
d
r
aw
b
ac
k
s
in
clu
d
e
t
h
e
in
clu
s
io
n
o
f
o
n
ly
th
e
f
ir
s
t
an
d
s
ec
o
n
d
c
o
n
v
o
lu
tio
n
al
lev
els
as
co
n
tr
ib
u
tin
g
v
ar
ia
b
les.
No
n
eth
eless
,
UE
D
-
I
L
O
v
ar
iab
le
o
p
tim
izatio
n
s
h
o
ws
f
u
tu
r
e
lear
n
in
g
ca
p
a
b
ilit
y
an
d
co
u
ld
a
n
aly
ze
tr
ain
in
g
an
d
tes
t
d
ata
s
ets
o
f
v
ar
ie
d
s
izes.
Fu
tu
r
e
r
esear
ch
is
co
n
ce
n
tr
ated
o
n
th
e
id
ea
l
s
ize
o
f
d
ata
r
eg
io
n
s
in
th
e
g
en
er
atio
n
o
f
n
ew
ar
ch
itectu
r
al
s
tr
u
ctu
r
es
u
s
in
g
d
ee
p
lear
n
in
g
alg
o
r
ith
m
s
.
Fu
r
t
h
er
m
o
r
e,
th
e
d
is
p
ar
ity
b
etwe
en
m
alig
n
an
t
a
n
d
b
e
n
ig
n
d
atasets
r
ep
r
esen
ts
a
wea
k
n
ess
o
f
th
e
B
r
ea
k
His
d
ataset.
As
a
r
esu
lt,
in
f
u
tu
r
e
s
tu
d
ies,
th
e
y
will
u
s
e
th
e
(
GAN)
m
eth
o
d
to
ex
ten
d
t
h
e
b
en
i
g
n
d
ata
b
ase.
ACK
NO
WL
E
DG
E
M
E
NT
W
e
wo
u
ld
lik
e
to
ex
p
r
ess
o
u
r
s
in
ce
r
e
g
r
atitu
d
e
to
all
th
o
s
e
wh
o
h
av
e
s
u
p
p
o
r
ted
an
d
co
n
tr
ib
u
ted
to
th
is
r
esear
ch
p
r
o
ject.
Prim
ar
il
y
,
we
ex
ten
d
o
u
r
h
ea
r
tf
elt
th
an
k
s
to
o
u
r
g
u
id
e
f
o
r
h
e
r
u
n
wav
er
in
g
g
u
id
a
n
ce
,
in
v
alu
ab
le
in
s
ig
h
ts
,
an
d
en
c
o
u
r
ag
em
en
t th
r
o
u
g
h
o
u
t th
e
r
esea
r
ch
p
r
o
ce
s
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
6
13
-
1
6
19
1618
RE
F
E
R
E
NC
E
S
[
1
]
M
.
M
.
S
r
i
k
a
n
t
a
m
u
r
t
h
y
,
V
.
P
.
S
.
R
a
l
l
a
b
a
n
d
i
,
D
.
B
.
D
u
d
e
k
u
l
a
,
S
.
N
a
t
a
r
a
j
a
n
,
a
n
d
J.
P
a
r
k
,
“
C
l
a
ss
i
f
i
c
a
t
i
o
n
o
f
b
e
n
i
g
n
a
n
d
m
a
l
i
g
n
a
n
t
su
b
t
y
p
e
s
o
f
b
r
e
a
st
c
a
n
c
e
r
h
i
s
t
o
p
a
t
h
o
l
o
g
y
i
ma
g
i
n
g
u
si
n
g
h
y
b
r
i
d
C
N
N
-
LS
TM
b
a
se
d
t
r
a
n
sf
e
r
l
e
a
r
n
i
n
g
,
”
B
MC
Me
d
i
c
a
l
I
m
a
g
i
n
g
,
v
o
l
.
2
3
,
n
o
.
1
,
p
.
1
9
,
J
a
n
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
8
6
/
s
1
2
8
8
0
-
0
2
3
-
0
0
9
6
4
-
0.
[
2
]
D
.
Y
u
,
J
.
Li
n
,
T.
C
a
o
,
Y
.
C
h
e
n
,
M
.
Li
,
a
n
d
X
.
Zh
a
n
g
,
“
S
E
C
S
:
a
n
e
f
f
e
c
t
i
v
e
C
N
N
j
o
i
n
t
c
o
n
s
t
r
u
c
t
i
o
n
st
r
a
t
e
g
y
f
o
r
b
r
e
a
st
c
a
n
c
e
r
h
i
s
t
o
p
a
t
h
o
l
o
g
i
c
a
l
i
ma
g
e
c
l
a
ss
i
f
i
c
a
t
i
o
n
,
”
J
o
u
r
n
a
l
o
f
K
i
n
g
S
a
u
d
U
n
i
v
e
rsi
t
y
-
C
o
m
p
u
t
e
r
a
n
d
I
n
f
o
rm
a
t
i
o
n
S
c
i
e
n
c
e
s
,
v
o
l
.
3
5
,
n
o
.
2
,
p
p
.
8
1
0
–
8
2
0
,
F
e
b
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
j
k
s
u
c
i
.
2
0
2
3
.
0
1
.
0
1
7
.
[
3
]
S
.
M
a
j
u
m
d
a
r
,
P
.
P
r
a
m
a
n
i
k
,
a
n
d
R
.
S
a
r
k
a
r
,
“
G
a
m
ma
f
u
n
c
t
i
o
n
b
a
s
e
d
e
n
s
e
m
b
l
e
o
f
C
N
N
m
o
d
e
l
s
f
o
r
b
r
e
a
s
t
c
a
n
c
e
r
d
e
t
e
c
t
i
o
n
i
n
h
i
st
o
p
a
t
h
o
l
o
g
y
i
ma
g
e
s
,
”
Ex
p
e
rt
S
y
st
e
m
s
w
i
t
h
A
p
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
2
1
3
,
p
.
1
1
9
0
2
2
,
M
a
r
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
sw
a
.
2
0
2
2
.
1
1
9
0
2
2
.
[
4
]
T.
A
r
a
ú
j
o
e
t
a
l
.
,
“
C
l
a
ss
i
f
i
c
a
t
i
o
n
o
f
b
r
e
a
st
c
a
n
c
e
r
h
i
st
o
l
o
g
y
i
ma
g
e
s
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
s,”
PL
O
S
O
N
E
,
v
o
l
.
1
2
,
n
o
.
6
,
p
.
e
0
1
7
7
5
4
4
,
J
u
n
.
2
0
1
7
,
d
o
i
:
1
0
.
1
3
7
1
/
j
o
u
r
n
a
l
.
p
o
n
e
.
0
1
7
7
5
4
4
.
[
5
]
D
.
B
a
n
u
ma
t
h
y
,
O
.
I
b
r
a
h
i
m
K
h
a
l
a
f
,
C
.
A
.
T
.
R
o
m
e
r
o
,
P
.
V.
R
a
j
a
,
a
n
d
D
.
K.
S
h
a
r
ma,
“
B
r
e
a
s
t
c
a
l
c
i
f
i
c
a
t
i
o
n
s
a
n
d
h
i
s
t
o
p
a
t
h
o
l
o
g
i
c
a
l
a
n
a
l
y
si
s
o
n
t
u
mo
u
r
d
e
t
e
c
t
i
o
n
b
y
C
N
N
,
”
C
o
m
p
u
t
e
r
S
y
st
e
m
s
S
c
i
e
n
c
e
a
n
d
En
g
i
n
e
e
ri
n
g
,
v
o
l
.
4
4
,
n
o
.
1
,
p
p
.
5
9
5
–
6
1
2
,
2
0
2
3
,
d
o
i
:
1
0
.
3
2
6
0
4
/
c
sse
.
2
0
2
3
.
0
2
5
6
1
1
.
[
6
]
P
.
A
g
a
r
w
a
l
,
A
.
Y
a
d
a
v
,
a
n
d
P
.
M
a
t
h
u
r
,
“
B
r
e
a
st
C
a
n
c
e
r
p
r
e
d
i
c
t
i
o
n
o
n
b
r
e
a
k
h
i
s
d
a
t
a
s
e
t
u
s
i
n
g
d
e
e
p
C
N
N
a
n
d
t
r
a
n
sf
e
r
l
e
a
r
n
i
n
g
mo
d
e
l
,
”
i
n
L
e
c
t
u
re
N
o
t
e
s
i
n
N
e
t
w
o
r
k
s
a
n
d
S
y
st
e
m
s
,
v
o
l
.
2
3
8
,
2
0
2
2
,
p
p
.
7
7
–
8
8
.
[
7
]
D
.
P
.
B
h
a
u
sa
h
e
b
a
n
d
K
.
L.
K
a
s
h
y
a
p
,
“
D
e
t
e
c
t
i
o
n
a
n
d
c
l
a
ss
i
f
i
c
a
t
i
o
n
o
f
b
r
e
a
st
c
a
n
c
e
r
a
v
a
i
l
i
n
g
d
e
e
p
c
a
n
i
d
o
p
t
i
m
i
z
a
t
i
o
n
b
a
s
e
d
d
e
e
p
C
N
N
,
”
M
u
l
t
i
m
e
d
i
a
T
o
o
l
s
a
n
d
A
p
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
8
2
,
n
o
.
1
2
,
p
p
.
1
8
0
1
9
–
1
8
0
3
7
,
M
a
y
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s1
1
0
4
2
-
022
-
1
4
2
6
8
-
y.
[
8
]
J.
H
u
a
n
g
e
t
a
l
.
,
“
B
M
-
N
e
t
:
C
N
N
-
b
a
se
d
M
o
b
i
l
e
N
e
t
-
V
3
a
n
d
b
i
l
i
n
e
a
r
st
r
u
c
t
u
r
e
f
o
r
b
r
e
a
s
t
c
a
n
c
e
r
d
e
t
e
c
t
i
o
n
i
n
w
h
o
l
e
sl
i
d
e
i
m
a
g
e
s,
”
Bi
o
e
n
g
i
n
e
e
r
i
n
g
,
v
o
l
.
9
,
n
o
.
6
,
p
.
2
6
1
,
J
u
n
.
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
b
i
o
e
n
g
i
n
e
e
r
i
n
g
9
0
6
0
2
6
1
.
[
9
]
T.
P
.
La
t
c
h
o
u
m
i
a
n
d
L.
P
a
r
t
h
i
b
a
n
,
“
Q
u
a
s
i
o
p
p
o
s
i
t
i
o
n
a
l
d
r
a
g
o
n
f
l
y
a
l
g
o
r
i
t
h
m
f
o
r
l
o
a
d
b
a
l
a
n
c
i
n
g
i
n
c
l
o
u
d
c
o
m
p
u
t
i
n
g
e
n
v
i
r
o
n
me
n
t
,
”
Wi
re
l
e
ss
Pe
r
so
n
a
l
C
o
m
m
u
n
i
c
a
t
i
o
n
s
,
v
o
l
.
1
2
2
,
n
o
.
3
,
p
p
.
2
6
3
9
–
2
6
5
6
,
F
e
b
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
1
2
7
7
-
021
-
0
9
0
2
2
-
w.
[
1
0
]
T.
P
.
L
a
t
c
h
o
u
mi
,
R
.
S
w
a
t
h
i
,
P
.
V
i
d
y
a
sr
i
,
a
n
d
K
.
B
a
l
a
m
u
r
u
g
a
n
,
“
D
e
v
e
l
o
p
n
e
w
a
l
g
o
r
i
t
h
m
t
o
i
m
p
r
o
v
e
s
a
f
e
t
y
O
n
W
M
S
N
i
n
h
e
a
l
t
h
d
i
s
e
a
se
m
o
n
i
t
o
r
i
n
g
,
”
i
n
2
0
2
2
I
n
t
e
r
n
a
t
i
o
n
a
l
M
o
b
i
l
e
a
n
d
Em
b
e
d
d
e
d
T
e
c
h
n
o
l
o
g
y
C
o
n
f
e
r
e
n
c
e
(
ME
C
O
N
)
,
M
a
r
.
2
0
2
2
,
p
p
.
3
5
7
–
3
6
2
,
d
o
i
:
1
0
.
1
1
0
9
/
M
E
C
O
N
5
3
8
7
6
.
2
0
2
2
.
9
7
5
2
1
7
8
.
[
1
1
]
Y
.
S
a
h
u
,
A
.
Tr
i
p
a
t
h
i
,
R
.
K
.
G
u
p
t
a
,
P
.
G
a
u
t
a
m,
R
.
K
.
P
a
t
e
r
i
y
a
,
a
n
d
A
.
G
u
p
t
a
,
“
A
C
N
N
-
S
V
M
b
a
se
d
c
o
m
p
u
t
e
r
a
i
d
e
d
d
i
a
g
n
o
s
i
s
o
f
b
r
e
a
s
t
c
a
n
c
e
r
u
s
i
n
g
h
i
st
o
g
r
a
m
K
-
mea
n
s
s
e
g
m
e
n
t
a
t
i
o
n
t
e
c
h
n
i
q
u
e
,
”
M
u
l
t
i
m
e
d
i
a
T
o
o
l
s
a
n
d
A
p
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
8
2
,
n
o
.
9
,
p
p
.
1
4
0
5
5
–
1
4
0
7
5
,
A
p
r
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
1
0
4
2
-
0
2
2
-
1
3
8
0
7
-
x.
[
1
2
]
Y
.
Q
i
,
X
.
L
i
u
,
H
.
Li
,
M
.
L
i
u
,
a
n
d
W
.
L
i
,
“
D
i
s
c
r
e
t
e
w
a
v
e
l
e
t
t
r
a
n
sf
o
r
m
-
b
a
se
d
C
N
N
f
o
r
b
r
e
a
st
c
a
n
c
e
r
c
l
a
ssi
f
i
c
a
t
i
o
n
f
r
o
m
h
i
s
t
o
p
a
t
h
o
l
o
g
y
i
ma
g
e
s
,
”
i
n
L
e
c
t
u
re
N
o
t
e
s
i
n
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
(
i
n
c
l
u
d
i
n
g
su
b
s
e
ri
e
s
L
e
c
t
u
r
e
N
o
t
e
s
i
n
A
rt
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
a
n
d
L
e
c
t
u
re
N
o
t
e
s
i
n
Bi
o
i
n
f
o
rm
a
t
i
c
s)
,
v
o
l
.
1
3
6
5
5
LN
C
S
,
2
0
2
3
,
p
p
.
3
2
6
–
3
4
0
.
[
1
3
]
H
.
U
.
K
h
a
n
,
B
.
R
a
z
a
,
M
.
H
.
S
h
a
h
,
S
.
M
.
U
sam
a
,
P
.
Ti
w
a
r
i
,
a
n
d
S
.
S
.
B
a
n
d
,
“
S
M
D
e
t
e
c
t
o
r
:
s
mal
l
mi
t
o
t
i
c
d
e
t
e
c
t
o
r
i
n
h
i
st
o
p
a
t
h
o
l
o
g
y
i
ma
g
e
s
u
si
n
g
f
a
st
e
r
R
-
C
N
N
w
i
t
h
d
i
l
a
t
e
d
c
o
n
v
o
l
u
t
i
o
n
s
i
n
b
a
c
k
b
o
n
e
m
o
d
e
l
,
”
Bi
o
m
e
d
i
c
a
l
S
i
g
n
a
l
Pr
o
c
e
ss
i
n
g
a
n
d
C
o
n
t
r
o
l
,
v
o
l
.
8
1
,
p
.
1
0
4
4
1
4
,
M
a
r
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
b
sp
c
.
2
0
2
2
.
1
0
4
4
1
4
.
[
1
4
]
Y
.
A
l
q
a
h
t
a
n
i
,
U
.
M
a
n
d
a
w
k
a
r
,
A
.
S
h
a
r
ma,
M
.
N
.
S
.
H
a
sa
n
,
M
.
H
.
K
u
l
k
a
r
n
i
,
a
n
d
R
.
S
u
g
u
mar
,
“
B
r
e
a
s
t
c
a
n
c
e
r
p
a
t
h
o
l
o
g
i
c
a
l
i
ma
g
e
c
l
a
ss
i
f
i
c
a
t
i
o
n
b
a
s
e
d
o
n
t
h
e
mu
l
t
i
sc
a
l
e
C
N
N
sq
u
e
e
z
e
m
o
d
e
l
,
”
C
o
m
p
u
t
a
t
i
o
n
a
l
I
n
t
e
l
l
i
g
e
n
c
e
a
n
d
N
e
u
r
o
sc
i
e
n
c
e
,
v
o
l
.
2
0
2
2
,
p
p
.
1
–
1
1
,
A
u
g
.
2
0
2
2
,
d
o
i
:
1
0
.
1
1
5
5
/
2
0
2
2
/
7
0
7
5
4
0
8
.
[
1
5
]
E.
M
.
R
o
o
p
a
D
e
v
i
,
R
.
S
h
a
n
t
h
a
k
u
mar
i
,
R
.
R
a
j
a
d
e
v
i
,
K
.
I
.
S.
S
h
r
e
e
,
R
.
S
h
a
k
k
i
r
u
n
,
a
n
d
K
.
P
o
o
r
v
a
j
a
,
“
I
mb
a
l
a
n
c
e
d
b
r
e
a
st
c
a
n
c
e
r
p
r
e
d
i
c
t
i
o
n
u
si
n
g
C
N
N
t
h
r
o
u
g
h
m
u
l
t
i
-
n
e
t
f
r
a
mew
o
r
k
,
”
i
n
2
0
2
3
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
C
o
m
p
u
t
e
r
C
o
m
m
u
n
i
c
a
t
i
o
n
a
n
d
I
n
f
o
rm
a
t
i
c
s (I
C
C
C
I
)
,
J
a
n
.
2
0
2
3
,
p
p
.
1
–
7
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
C
C
I
5
6
7
4
5
.
2
0
2
3
.
1
0
1
2
8
5
4
3
.
[
1
6
]
S
.
A
n
j
u
m
e
t
a
l
.
,
“
L
u
n
g
c
a
n
c
e
r
c
l
a
s
si
f
i
c
a
t
i
o
n
i
n
h
i
st
o
p
a
t
h
o
l
o
g
y
i
ma
g
e
s
u
si
n
g
m
u
l
t
i
r
e
so
l
u
t
i
o
n
e
f
f
i
c
i
e
n
t
n
e
t
s,
”
C
o
m
p
u
t
a
t
i
o
n
a
l
I
n
t
e
l
l
i
g
e
n
c
e
a
n
d
N
e
u
ro
s
c
i
e
n
c
e
,
v
o
l
.
2
0
2
3
,
n
o
.
1
,
p
p
.
1
–
1
2
,
J
a
n
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
5
5
/
2
0
2
3
/
7
2
8
2
9
4
4.
[
1
7
]
P
.
P
u
j
a
r
,
A
.
K
u
m
a
r
,
a
n
d
V
.
K
u
mar
,
“
Ef
f
i
c
i
e
n
t
p
l
a
n
t
l
e
a
f
d
e
t
e
c
t
i
o
n
t
h
r
o
u
g
h
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
b
a
se
d
o
n
c
o
r
n
l
e
a
f
i
ma
g
e
c
l
a
ss
i
f
i
c
a
t
i
o
n
,
”
I
AE
S
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
Ar
t
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
(
I
J
-
AI
)
,
v
o
l
.
1
3
,
n
o
.
1
,
p
p
.
1
1
3
9
–
1
1
4
8
,
M
a
r
.
2
0
2
4
,
d
o
i
:
1
0
.
1
1
5
9
1
/
i
j
a
i
.
v
1
3
.
i
1
.
p
p
1
1
3
9
-
1
1
4
8
.
[
1
8
]
S
.
H
.
S
r
e
e
d
h
a
r
a
,
V
.
K
u
mar,
a
n
d
S
.
S
a
l
ma
,
“
Ef
f
i
c
i
e
n
t
b
i
g
d
a
t
a
c
l
u
s
t
e
r
i
n
g
u
si
n
g
a
d
h
o
c
f
u
z
z
y
c
me
a
n
s
a
n
d
a
u
t
o
-
e
n
c
o
d
e
r
C
N
N
,
”
i
n
L
e
c
t
u
re
N
o
t
e
s
i
n
N
e
t
w
o
r
k
s
a
n
d
S
y
st
e
m
s
,
v
o
l
.
5
6
3
,
2
0
2
3
,
p
p
.
3
5
3
–
3
6
8
.
[
1
9
]
T.
P
.
La
t
c
h
o
u
m
i
,
T.
P
.
Ez
h
i
l
a
r
a
s
i
,
a
n
d
K
.
B
a
l
a
mu
r
u
g
a
n
,
“
B
i
o
-
i
n
sp
i
r
e
d
w
e
i
g
h
e
d
q
u
a
n
t
u
m
p
a
r
t
i
c
l
e
sw
a
r
m
o
p
t
i
m
i
z
a
t
i
o
n
a
n
d
smo
o
t
h
su
p
p
o
r
t
v
e
c
t
o
r
mac
h
i
n
e
e
n
sem
b
l
e
s
f
o
r
i
d
e
n
t
i
f
i
c
a
t
i
o
n
o
f
a
b
n
o
r
ma
l
i
t
i
e
s
i
n
med
i
c
a
l
d
a
t
a
,
”
S
N
A
p
p
l
i
e
d
S
c
i
e
n
c
e
s
,
v
o
l
.
1
,
n
o
.
1
0
,
p
.
1
1
3
7
,
O
c
t
.
2
0
1
9
,
d
o
i
:
1
0
.
1
0
0
7
/
s4
2
4
5
2
-
0
1
9
-
1
1
7
9
-
8.
[
2
0
]
B
.
A
b
u
n
a
sser
,
M
.
R
.
A
L
-
H
i
e
a
l
y
,
I
.
Za
q
o
u
t
,
a
n
d
S
.
A
b
u
-
N
a
s
e
r
,
“
C
o
n
v
o
l
u
t
i
o
n
n
e
u
r
a
l
n
e
t
w
o
r
k
f
o
r
b
r
e
a
st
c
a
n
c
e
r
d
e
t
e
c
t
i
o
n
a
n
d
c
l
a
ss
i
f
i
c
a
t
i
o
n
u
si
n
g
d
e
e
p
l
e
a
r
n
i
n
g
,
”
Asi
a
n
P
a
c
i
f
i
c
J
o
u
r
n
a
l
o
f
C
a
n
c
e
r
Pre
v
e
n
t
i
o
n
,
v
o
l
.
2
4
,
n
o
.
2
,
p
p
.
5
31
–
5
4
4
,
F
e
b
.
2
0
2
3
,
d
o
i
:
1
0
.
3
1
5
5
7
/
A
P
J
C
P
.
2
0
2
3
.
2
4
.
2
.
5
3
1
.
[
2
1
]
M
.
B
.
T
a
y
e
l
,
M
.
A
.
A
.
M
o
k
h
t
a
r
,
a
n
d
A
.
F
.
K
i
s
h
k
,
“
B
r
e
a
st
c
a
n
c
e
r
d
i
a
g
n
o
si
s
u
s
i
n
g
h
i
st
o
p
a
t
h
o
l
o
g
y
a
n
d
c
o
n
v
o
l
u
t
i
o
n
n
e
u
r
a
l
n
e
t
w
o
r
k
C
N
N
m
e
t
h
o
d
,
”
i
n
L
e
c
t
u
re
N
o
t
e
s
i
n
N
e
t
w
o
rks
a
n
d
S
y
st
e
m
s
,
v
o
l
.
4
7
3
,
2
0
2
3
,
p
p
.
5
8
5
–
6
0
0
.
[
2
2
]
C
.
R
.
P
r
a
s
a
d
,
B
.
A
r
u
n
,
S
.
A
m
u
l
y
a
,
P
.
A
b
b
o
j
u
,
S
.
K
o
l
l
e
m,
a
n
d
S
.
Y
a
l
a
b
a
k
a
,
“
B
r
e
a
st
c
a
n
c
e
r
c
l
a
ssi
f
i
c
a
t
i
o
n
u
si
n
g
C
N
N
w
i
t
h
t
r
a
n
sf
e
r
l
e
a
r
n
i
n
g
mo
d
e
l
s,
”
i
n
2
0
2
3
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
f
o
r
Ad
v
a
n
c
e
m
e
n
t
i
n
T
e
c
h
n
o
l
o
g
y
(
I
C
O
N
AT)
,
J
a
n
.
2
0
2
3
,
p
p
.
1
–
5
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
O
N
A
T5
7
1
3
7
.
2
0
2
3
.
1
0
0
8
0
1
4
8
.
[
2
3
]
M
.
M
.
Z
a
f
a
r
e
t
a
l
.
,
“
D
e
t
e
c
t
i
o
n
o
f
t
u
mo
u
r
i
n
f
i
l
t
r
a
t
i
n
g
l
y
m
p
h
o
c
y
t
e
s
i
n
C
D
3
a
n
d
C
D
8
s
t
a
i
n
e
d
h
i
s
t
o
p
a
t
h
o
l
o
g
i
c
a
l
i
ma
g
e
s
u
s
i
n
g
a
t
w
o
-
p
h
a
se
d
e
e
p
C
N
N
,
”
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
7
,
p
.
1
0
2
6
7
6
,
M
a
r
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
p
d
p
d
t
.
2
0
2
1
.
1
0
2
6
7
6
.
[
2
4
]
N
.
Jo
s
e
p
h
a
n
d
R
.
G
u
p
t
a
,
“
D
u
a
l
m
u
l
t
i
-
sc
a
l
e
C
N
N
f
o
r
m
u
l
t
i
-
l
a
y
e
r
b
r
e
a
st
c
a
n
c
e
r
c
l
a
ssi
f
i
c
a
t
i
o
n
a
t
mu
l
t
i
-
r
e
so
l
u
t
i
o
n
,
”
i
n
2
0
2
2
4
t
h
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
A
d
v
a
n
c
e
s
i
n
C
o
m
p
u
t
i
n
g
,
C
o
m
m
u
n
i
c
a
t
i
o
n
C
o
n
t
r
o
l
a
n
d
N
e
t
w
o
rki
n
g
(
I
C
A
C
3
N
)
,
D
e
c
.
2
0
2
2
,
p
p
.
6
1
3
–
6
1
8
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
A
C
3
N
5
6
6
7
0
.
2
0
2
2
.
1
0
0
7
3
9
8
5
.
[
2
5
]
J.
G
.
M
e
l
e
k
o
o
d
a
p
p
a
t
t
u
,
A
.
S
.
D
h
a
s
,
B
.
K
.
K
a
n
d
a
t
h
i
l
,
a
n
d
K
.
S
.
A
d
a
r
s
h
,
“
B
r
e
a
st
c
a
n
c
e
r
d
e
t
e
c
t
i
o
n
i
n
m
a
mm
o
g
r
a
m:
c
o
m
b
i
n
i
n
g
mo
d
i
f
i
e
d
C
N
N
a
n
d
t
e
x
t
u
r
e
f
e
a
t
u
r
e
b
a
sed
a
p
p
r
o
a
c
h
,
”
J
o
u
rn
a
l
o
f
Am
b
i
e
n
t
I
n
t
e
l
l
i
g
e
n
c
e
a
n
d
H
u
m
a
n
i
z
e
d
C
o
m
p
u
t
i
n
g
,
v
o
l
.
1
4
,
n
o
.
9
,
p
p
.
1
1
3
9
7
–
1
1
4
0
6
,
S
e
p
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
2
6
5
2
-
0
2
2
-
0
3
7
1
3
-
3.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Dev
elo
p
ed
imp
r
o
ve
d
lio
n
o
p
timiz
a
tio
n
fo
r
b
r
ea
s
t c
a
n
ce
r
…
(
P
a
tta
n
M
.
D
.
A
li K
h
a
n
)
1619
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Pa
tta
n
M.
D.
Ali
K
h
a
n
e
a
rn
e
d
h
is Bac
h
e
lo
rs o
f
Tec
h
n
o
l
o
g
y
B.
Tec
h
d
e
g
re
e
in
CS
E
fr
o
m
JN
TUA
i
n
2
0
0
5
.
He
h
a
s
o
b
tain
e
d
h
is
M
a
ste
r’s
d
e
g
re
e
in
M
.
E
(CS
E)
fr
o
m
S
t
.
P
e
ter’s
Un
iv
e
rsity
in
2
0
1
2
.
An
d
c
u
rre
n
tl
y
h
e
is
a
Re
se
a
r
c
h
S
c
h
o
lar
a
t
B.
S
.
Ab
d
u
r
Ra
h
m
a
n
Cre
sc
e
n
t
In
stit
u
te
o
f
S
c
ien
c
e
a
n
d
Tec
h
n
o
l
o
g
y
(
d
e
e
m
e
d
to
b
e
u
n
iv
e
rsity
)
d
o
in
g
h
is
P
h
.
D
.
in
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
E
n
g
in
e
e
rin
g
.
He
h
a
s
a
tt
e
n
d
e
d
m
a
n
y
wo
rk
sh
o
p
s
a
n
d
in
d
u
c
ti
o
n
p
ro
g
ra
m
s
c
o
n
d
u
c
ted
b
y
v
a
ri
o
u
s
u
n
iv
e
rsiti
e
s.
His
a
re
a
s
o
f
in
tere
st
a
re
m
a
c
h
in
e
lea
rn
in
g
a
n
d
ima
g
e
p
ro
c
e
ss
in
g
.
He
c
a
n
b
e
c
o
n
t
a
c
ted
a
t
e
m
a
il
:
a
li
k
_
1
2
@re
d
iffma
il
.
c
o
m
.
Dr
.
Xa
v
ier
Ar
p
u
th
a
Ra
t
h
in
a
is
a
n
As
so
c
iate
P
r
o
fe
ss
o
r
i
n
C
o
m
p
u
ter
S
c
ien
c
e
a
n
d
E
n
g
i
n
e
e
rin
g
D
e
p
a
rtme
n
t
a
t
S
c
h
o
o
l
o
f
S
CI
M
S
o
f
B.
S
.
Ab
d
u
r
R
a
h
m
a
n
Cre
sc
e
n
t
In
stit
u
te
o
f
S
c
ie
n
c
e
a
n
d
Tec
h
n
o
l
o
g
y
(
d
e
e
m
e
d
to
b
e
u
n
iv
e
rsit
y
)
Ch
e
n
n
a
i
with
a
n
e
x
p
e
rien
c
e
o
f
2
7
y
e
a
rs
in
Tea
c
h
in
g
.
S
h
e
d
i
d
h
e
r
B.
E
in
El
e
c
tro
n
ics
C
o
m
m
u
n
i
c
a
ti
o
n
E
n
g
i
n
e
e
rin
g
a
n
d
M
a
ste
r’s
d
e
g
re
e
i
n
C
o
m
p
u
ter
S
c
i
e
n
c
e
a
n
d
En
g
in
e
e
ri
n
g
,
a
n
d
P
h
.
D
.
in
C
o
m
p
u
ter
S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
.
He
r
a
re
a
s
o
f
in
tere
st
a
re
ima
g
e
p
ro
c
e
ss
in
g
,
e
m
o
ti
o
n
a
l
in
telli
g
e
n
c
e
,
a
n
d
sp
e
e
c
h
p
ro
c
e
ss
in
g
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
x
a
ra
th
in
a
@c
re
sc
e
n
t.
e
d
u
c
a
ti
o
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.