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849
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s
s
if
i
c
a
t
i
o
n
o
f
b
r
e
a
s
t
c
a
n
c
e
r
t
um
o
r
s
a
r
e
a
n
e
s
s
e
n
t
i
a
l
to
g
oo
d
tr
e
a
t
m
e
n
t
pl
a
nni
ng
a
s
we
l
l
a
s
pa
t
i
e
n
t
pr
o
gn
o
s
i
s
.
P
r
e
d
i
c
t
i
o
n
o
f
c
a
n
c
e
r
r
e
c
ur
r
e
n
c
e
p
l
a
y
s
a
vi
t
a
l
r
o
l
e
i
n
de
t
e
r
m
i
ni
ng
a
p
pr
o
p
r
i
a
t
e
f
o
l
l
o
w
-
up
c
a
r
e
f
o
r
pa
t
i
e
n
t
s
[
5]
.
T
h
e
m
a
i
n
o
bj
e
t
i
v
e
o
f
t
h
e
pr
o
p
o
s
e
d
s
y
s
t
e
m
i
s
t
o
g
r
o
w
t
h
a
r
o
b
us
t
a
n
d
a
c
c
ur
a
t
e
m
o
de
l
f
o
r
b
r
e
a
s
t
c
a
n
c
e
r
c
l
a
s
s
i
f
i
c
a
t
i
o
n
a
n
d
r
e
c
ur
r
e
n
c
e
d
i
a
g
n
o
s
i
s
,
ut
i
l
i
z
i
ng
bi
o
-
i
n
s
p
i
r
e
d
a
l
g
o
r
i
t
hm
s
.
De
e
p
l
e
a
r
ni
ng
t
e
c
h
ni
qu
e
s
s
uc
h
a
s
t
h
e
c
o
n
v
o
l
ut
i
o
n
a
l
n
e
ur
a
l
n
e
t
wo
r
k
(
C
NN
)
m
o
de
l
a
r
e
us
e
d
f
o
r
f
e
a
t
ur
e
e
x
t
r
a
c
t
i
o
n
[
6]
.
S
C
S
O
o
p
t
i
m
i
z
a
t
i
o
n
t
e
c
hni
que
s
a
r
e
us
e
d
f
o
r
f
e
a
t
ur
e
o
p
t
i
m
i
z
a
t
i
o
n
.
S
upp
o
r
t
v
e
c
to
r
m
a
c
hi
n
e
(
S
VM
)
,
k
-
n
e
a
r
e
s
t
n
e
i
g
hb
o
r
(
K
NN
)
,
r
a
n
do
m
f
o
r
e
s
t
(
RF
)
,
a
n
d
v
ot
i
n
g
c
l
a
s
s
if
i
e
r
(
VC
)
m
a
c
hi
ne
l
e
a
r
ni
ng
a
l
go
r
i
t
hm
s
a
r
e
a
pp
l
i
e
d
f
o
r
b
r
e
a
s
t
c
a
n
c
e
r
c
l
a
s
s
if
i
c
a
t
i
o
n
.
Ge
n
e
t
i
c
a
l
go
r
i
t
hm
s
a
r
e
us
e
d
to
o
p
t
i
mi
z
e
t
h
e
c
l
a
s
s
if
i
c
a
t
i
o
n
m
o
de
l
[
7]
.
T
h
e
c
o
n
t
r
i
b
ut
i
o
ns
o
f
pr
o
p
o
s
e
d
m
e
t
h
o
d
i
s
s
pe
c
i
f
ied:
de
v
e
l
o
p
a
b
r
e
a
s
t
c
a
n
c
e
r
c
l
a
s
s
if
i
c
a
t
i
o
n
m
o
de
l
c
a
pa
bl
e
o
f
a
c
c
ur
a
t
e
l
y
d
i
s
t
i
n
gu
i
s
hi
ng
be
t
we
e
n
m
a
li
g
n
a
n
t
a
n
d
b
e
ni
g
n
b
r
e
a
s
t
t
um
o
r
s
b
a
s
e
d
o
n
d
i
v
e
r
s
e
c
l
i
n
i
c
a
l
a
n
d
d
i
a
g
n
o
s
t
i
c
f
e
a
t
ur
e
s
.
Ut
i
li
z
e
d
a
ge
n
e
t
i
c
a
l
go
r
i
t
hm
to
o
p
t
i
mi
z
e
t
h
e
c
l
a
s
s
if
i
c
a
t
i
o
n
m
o
de
l
’
s
pe
r
f
o
r
m
a
n
c
e
,
e
ns
ur
i
n
g
hi
g
h
a
c
c
ur
a
c
y
,
pr
e
c
i
s
i
o
n
,
a
n
d
r
e
c
a
ll
.
A
pp
ly
bi
o
-
i
ns
p
i
r
e
d
a
l
go
r
i
t
hm
s
s
u
c
h
a
s
s
a
n
d
c
a
t
s
wa
r
m
o
p
t
i
mi
z
a
t
i
o
n
(
S
C
S
O)
to
i
de
n
t
i
f
y
s
i
g
nif
i
c
a
n
t
r
e
c
u
r
r
e
n
c
e
pr
e
d
i
c
t
or
s
by
a
na
l
y
z
i
ng
a
w
i
de
r
a
n
ge
o
f
pa
t
i
e
n
t
-
s
pe
c
i
f
i
c
f
a
c
t
o
r
s
,
i
n
c
l
ud
i
ng
ge
n
e
t
i
c
m
a
r
ke
r
s
,
t
r
e
a
t
m
e
n
t
hi
s
t
or
y
,
a
n
d
po
s
t
-
t
r
e
a
t
m
e
n
t
f
o
l
l
o
w
-
up
da
t
a
.
T
h
e
pa
pe
r
i
s
s
t
r
uc
t
ur
e
d
a
s
f
o
l
l
o
w
s
:
s
e
c
t
i
o
n
2
d
i
s
c
u
s
s
e
s
e
xi
s
t
i
ng
a
n
d
pr
i
o
r
r
e
s
e
a
r
c
h
e
f
f
o
r
t
s
,
p
r
o
vi
d
i
ng
a
n
o
v
e
r
vi
e
w
o
f
r
e
l
a
t
e
d
w
o
r
k
.
S
e
c
t
i
o
n
3
o
u
t
l
i
n
e
s
t
h
e
da
t
a
s
e
t
us
e
d
i
n
t
hi
s
s
t
ud
y
a
n
d
t
h
e
m
e
t
h
o
ds
a
n
d
m
e
t
h
o
do
l
o
g
y
o
f
t
h
e
p
r
o
p
o
s
e
d
s
t
udy
.
S
e
c
t
i
o
n
4
i
s
de
d
i
c
a
t
e
d
to
pr
e
s
e
n
t
i
n
g
t
h
e
r
e
s
u
l
t
s
a
n
d
c
o
n
duc
t
i
n
g
a
n
e
v
a
l
ua
t
i
o
n
a
na
ly
s
i
s
o
f
t
h
e
pe
r
f
o
r
m
a
nc
e
.
F
i
n
a
ll
y
,
i
n
s
e
c
t
i
o
n
5
,
c
o
n
c
l
u
s
i
o
n
s
ba
s
e
d
o
n
t
h
e
f
i
nd
i
ngs
o
f
t
h
i
s
s
t
ud
y
a
r
e
m
a
de
.
M
a
ny
t
e
c
hn
o
l
o
g
i
e
s
a
r
e
e
m
e
r
g
i
ng
i
n
c
o
m
put
e
r
s
c
i
e
n
c
e
a
n
d
ha
v
e
b
e
e
n
ut
i
l
i
z
e
d
i
n
t
h
e
m
e
d
i
c
a
l
f
i
e
l
d
i
n
r
e
c
e
n
t
y
e
a
r
s
.
S
o
,
i
t
i
s
e
s
s
e
n
t
i
a
l
t
o
un
de
r
s
t
a
n
d
whi
c
h
t
e
c
hn
o
l
o
g
y
m
us
t
b
e
a
pp
li
e
d
to
di
f
f
e
r
e
n
t
d
i
s
e
a
s
e
s
f
o
un
d
i
n
t
h
e
m
e
d
i
c
a
l
f
i
e
l
d
[
8
]
.
M
a
c
hi
ne
l
e
a
r
ni
ng
(
M
L
)
a
n
d
a
r
t
i
f
i
c
i
a
l
i
n
t
e
l
li
ge
nc
e
(
A
I
)
a
r
e
e
m
e
r
g
i
n
g
t
e
c
hn
o
l
o
gi
e
s
us
e
d
i
n
t
he
m
e
d
i
c
a
l
f
i
e
l
d
f
o
r
a
l
l
k
i
nd
s
o
f
d
i
s
e
a
s
e
s
a
n
d
c
a
n
c
e
r
s
t
h
r
o
ugh
o
u
t
r
e
c
e
n
t
y
e
a
r
s
[
9]
.
M
L
a
l
go
r
i
t
hm
s
a
r
e
us
e
d
a
s
b
o
t
h
c
l
a
s
s
if
i
e
r
s
a
n
d
r
e
gr
e
s
s
o
r
s
to
pr
e
di
c
t
t
h
e
pr
e
s
e
nc
e
o
f
c
a
n
c
e
r
by
g
i
v
i
ng
s
o
m
e
i
n
put
s
to
t
h
e
de
v
e
l
o
pe
d
m
o
de
l
[
10]
.
Ho
we
v
e
r
,
a
s
t
h
e
da
t
a
ge
n
e
r
a
t
e
d
i
n
t
h
e
m
e
d
i
c
a
l
f
i
e
l
d
i
s
qu
i
t
e
l
a
r
ge
a
n
d
o
f
hi
g
h
d
i
m
e
ns
i
o
n
,
t
h
e
pr
e
d
i
c
t
e
d
a
c
c
ur
a
c
y
i
s
b
e
c
o
m
i
ng
l
o
w,
i
n
a
dd
i
t
i
o
n
t
o
m
a
ny
e
r
r
o
r
s
i
n
t
h
e
de
v
e
l
o
pe
d
m
o
de
l
.
T
hi
s
l
e
a
d
s
t
o
t
h
e
us
e
o
f
B
i
o
-
i
ns
p
i
r
e
d
a
l
go
r
i
t
hm
s
to
l
e
s
s
e
n
t
h
e
hi
g
h
e
r
da
ta
di
m
e
ns
i
o
n
.
T
h
e
pa
pe
r
[
11
]
pr
o
p
o
s
e
s
a
n
M
L
m
o
de
l
f
o
r
t
h
e
c
l
a
s
s
if
i
c
a
t
i
o
n
o
f
b
r
e
a
s
t
c
a
n
c
e
r
.
T
h
r
o
ugh
o
u
t
t
h
e
pa
pe
r
[
12
]
,
a
n
a
uto
m
a
t
e
d
de
c
i
s
i
o
n
-
m
a
k
i
ng
p
i
pe
li
n
e
f
o
r
B
C
de
t
e
c
t
i
o
n
ha
s
b
e
e
n
pr
o
p
o
s
e
d,
i
n
c
o
r
po
r
a
t
i
n
g
M
L
a
l
go
r
i
t
hm
s
l
i
ke
ga
us
s
i
a
n
N
a
i
v
e
B
a
y
e
s
(
GN
B
)
,
R
F
,
XG
B
o
o
s
t
(
XG
B
)
,
A
da
B
o
o
s
t
(
A
dB
)
,
a
n
d
pr
e
-
pr
o
c
e
s
s
i
n
g
s
uc
h
a
s
o
ut
l
i
e
r
r
e
j
e
c
t
i
o
n
(
OR
)
a
n
d
a
tt
r
i
b
ut
e
s
e
l
e
c
t
i
o
n
(
A
S
)
to
i
nc
r
e
a
s
e
t
h
e
s
ur
vi
va
l
r
a
t
e
o
f
t
h
e
pa
t
i
e
n
t
s
uf
f
e
r
i
ng
f
r
o
m
B
C
[
13
].
T
h
e
pa
pe
r
[
14
]
p
r
o
p
o
s
e
d
a
c
o
m
put
e
r
-
a
i
de
d
e
n
s
e
mbl
e
m
e
t
h
o
d
f
o
r
di
a
g
n
o
s
i
n
g
B
C
us
i
ng
R
e
Ne
t
18
a
n
d
S
VM
.
A
pr
e
-
t
r
a
i
n
e
d
R
e
Ne
t
18
m
o
de
l
i
s
us
e
d
t
o
e
x
t
r
a
c
t
t
h
e
f
e
a
t
ur
e
s
f
r
o
m
t
h
e
X
-
r
a
y
i
m
a
ge
,
a
n
d
S
VM
i
s
u
t
i
li
z
e
d
i
n
d
i
a
g
n
o
s
i
n
g
c
a
n
c
e
r
.
T
h
e
pa
pe
r
’
s
a
ut
h
o
r
us
e
d
a
n
M
P
S
O
wi
t
h
a
n
S
VM
o
n
a
w
i
s
c
o
ns
i
n
b
r
e
a
s
t
c
a
nc
e
r
UC
I
d
a
ta
s
e
t
t
o
d
e
te
c
t
b
r
e
a
s
t
c
a
n
c
e
r
.
T
h
e
p
r
op
os
e
d
m
e
th
od
c
a
n
a
l
s
o
pi
n
p
o
i
n
t
u
s
e
f
ul
b
r
e
a
s
t
c
a
n
c
e
r
c
h
a
r
a
c
te
r
i
s
ti
c
s
[
1
5
]
.
T
h
e
pr
e
s
e
n
t
e
d
M
P
S
O
ut
i
l
i
z
e
s
a
n
a
da
pt
i
v
e
s
t
r
a
t
e
gy
to
e
nh
a
n
c
e
pe
r
f
o
r
m
a
n
c
e
w
hil
e
s
o
l
vi
ng
d
i
f
f
i
c
u
l
t
i
s
s
ue
s
a
n
d
pr
e
v
e
n
t
i
n
g
t
h
e
pr
e
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O.
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r
c
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ga
p
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B
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o
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l
go
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s
s
uc
h
a
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t
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o
n
,
S
C
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O
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a
r
t
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a
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n
d
b
r
a
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ns
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or
m
o
p
t
i
mi
z
a
t
i
o
n
a
l
go
r
i
t
hm
s
[1
6
]
.
T
h
e
s
t
udy
d
i
s
c
u
s
s
e
s
whi
c
h
f
e
a
t
ur
e
s
e
l
e
c
t
i
o
n
a
l
go
r
i
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hm
pe
r
f
o
r
m
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b
e
t
t
e
r
f
o
r
e
a
c
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l
a
s
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if
i
c
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t
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o
n
m
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h
o
d
a
n
d
pr
o
vi
de
s
hi
g
h
a
c
c
ur
a
c
y
[1
7
].
2.
B
R
E
A
S
T
C
AN
C
E
R
P
R
E
DI
C
T
I
O
N
U
S
I
N
G
GE
N
E
T
I
C
AL
G
ORI
T
H
M
AN
D
S
CS
O
AL
G
ORI
T
H
M
2.
1.
B
r
e
as
t
c
an
c
e
r
c
l
as
s
i
f
icat
ion
F
i
gur
e
1
e
x
p
l
a
i
ns
t
h
e
r
e
s
e
a
r
c
h
m
e
t
h
o
d
o
l
o
g
y
f
o
r
t
h
e
c
l
a
s
s
if
i
c
a
t
i
o
n
s
y
s
t
e
m
.
I
t
c
o
n
t
a
i
n
s
5
p
h
a
s
e
s
.
T
he
f
i
r
s
t
ph
a
s
e
o
f
t
hi
s
m
e
t
h
o
do
l
o
g
y
wa
s
t
h
e
da
t
a
-
c
o
l
l
e
c
t
i
n
g
ph
a
s
e
,
dur
i
n
g
w
hi
c
h
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nve
s
t
i
g
a
t
i
o
n
wa
s
ga
t
h
e
r
e
d
[
18
]
.
T
h
e
da
t
a
pr
e
pr
o
c
e
s
s
i
ng
s
t
a
ge
wa
s
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h
e
s
e
c
o
n
d
p
h
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s
e
whe
n
t
h
e
ga
t
h
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r
e
d
da
t
a
wa
s
c
o
m
bi
ne
d,
c
l
e
a
n
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d,
a
n
d
pr
o
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s
s
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d
to
m
a
ke
t
h
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da
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a
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t
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c
c
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pt
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bl
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f
o
r
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i
f
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o
n
pr
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d
i
c
t
i
o
n
,
a
s
s
h
o
wn
i
n
F
i
gur
e
1
.
Af
t
e
r
t
h
a
t
,
whi
l
e
s
t
i
ll
i
n
t
h
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s
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c
o
n
d
p
h
a
s
e
,
a
ge
ne
t
i
c
a
l
g
o
r
i
t
hm
[
19
]
wa
s
us
e
d
to
e
x
tr
a
c
t
f
e
a
t
ur
e
s
.
On
c
e
t
h
e
pr
e
pr
o
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e
s
s
i
n
g
p
h
a
s
e
[
20
]
(
P
h
a
s
e
2)
wa
s
c
o
m
p
l
e
t
e
,
t
h
e
da
t
a
we
r
e
pa
s
s
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d
o
v
e
r
to
P
h
a
s
e
3,
wh
e
r
e
t
h
e
y
we
r
e
us
e
d
to
b
u
i
l
d
m
o
de
l
s
b
ot
h
w
i
t
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n
d
w
i
t
h
o
ut
f
e
a
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ur
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e
x
t
r
a
c
t
i
o
n
[
21
]
.
P
h
a
s
e
4
i
nv
o
l
v
e
d
t
h
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e
v
a
l
ua
t
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o
f
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de
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o
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r
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s
ul
t
a
n
a
l
y
s
i
s
.
I
n
t
h
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f
i
na
l
p
h
a
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o
f
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r
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a
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h
,
a
c
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m
pa
r
i
s
o
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o
f
t
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a
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ur
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-
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b
a
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d
m
o
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m
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de
l
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m
p
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y
f
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a
t
ur
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s
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l
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c
t
i
o
n
wa
s
do
n
e
[
22
].
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
do
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s
i
a
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J
E
l
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N:
2
5
0
2
-
4
7
52
B
r
e
as
t
c
anc
e
r
pr
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diction
us
ing
ge
ne
ti
c
algor
it
hm
and
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and
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at
s
w
ar
m
opti
miz
ati
on
…
(
V
e
lu
Sange
e
tha
)
851
F
i
gur
e
1
.
R
e
s
e
a
r
c
h
m
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t
h
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do
l
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y
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r
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a
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s
if
i
c
a
t
i
o
n
s
y
s
t
e
m
2.
2.
D
at
as
e
t
d
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s
c
r
ip
t
ion
T
h
e
w
i
s
c
o
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g
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(
W
DB
C
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t
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d
b
r
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a
s
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c
a
n
c
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r
r
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s
e
a
r
c
h
[
23
]
.
Dr
.
W
i
ll
ia
m
H.
W
o
l
b
e
r
g
c
r
e
a
t
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d
da
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c
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put
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d
f
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d
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g
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d
i
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ge
s
o
f
b
r
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a
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t
m
a
s
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s
’
f
i
ne
n
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e
d
le
a
s
p
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r
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t
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s
(
F
N
A
)
.
T
h
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go
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l
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f
t
hi
s
wo
r
k
to
p
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c
t
wh
e
t
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r
a
gi
v
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us
)
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li
g
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a
n
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us
)
.
M
a
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m
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a
g
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to
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t
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s
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ur
r
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-
r
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ur
r
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c
e
.
T
h
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tot
a
l
n
u
m
be
r
o
f
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m
a
ge
s
us
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d
i
s
500,
o
f
w
hi
c
h
250
im
a
ge
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t
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a
t
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l
l
t
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ll
s
s
h
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nd
250
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h
o
w
n
o
n
-
r
e
c
ur
r
e
n
c
e
s
i
g
ns
[
24
].
2.
3.
Al
gor
it
h
m
s
2.
3.
1.
Genet
ic
al
gor
it
h
m
(
GA)
GA
i
s
a
s
e
a
r
c
h
a
n
d
o
p
t
i
mi
z
a
t
i
o
n
t
e
c
h
ni
que
[2
5
]
i
n
s
p
i
r
e
d
by
t
h
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e
v
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l
ut
i
o
n
a
n
d
n
a
t
ur
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t
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n
pr
o
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s
s
e
s
.
I
t
i
s
a
t
y
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n
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t
h
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s
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A
ge
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f
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ly
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r
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s
.
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c
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,
whi
c
h
i
s
a
n
a
l
o
go
us
to
t
h
e
DN
A
i
n
bi
o
l
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g
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c
a
l
t
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a
n
d
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m
m
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na
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g
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t
s
(
0s
a
n
d
1s
)
i
n
Al
go
r
i
t
hm
1
,
i
s
us
e
d
t
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r
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pr
e
s
e
n
t
e
a
c
h
s
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l
ut
i
o
n
i
n
t
h
e
po
pul
a
t
i
o
n
[
2
6
]
.
A
l
g
o
r
i
t
hm
1
.
Ge
n
e
t
i
c
_a
l
go
r
i
t
hm
Genetic_Algorithm()
{
Initialize random population
Evaluate the population
Generation=0
While the termination criterion is not satisfied
{
Generation=Generation + 1
Select good chromosomes by
reproduction procedure
Perform crossover with probability crossover (Pc)
Perform mutation with probability mutation (Pm)
Evaluate the population
}
}
2.
3.
2.
S
an
d
c
at
s
war
m
op
t
im
iz
at
ion
(
S
CSO
)
I
n
t
h
e
S
C
S
O
a
l
go
r
i
t
hm
[2
7
]
,
t
h
e
s
e
a
r
c
h
f
o
r
p
r
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y
i
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m
po
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r
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d
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e
a
c
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n
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t
’
s
a
bil
i
t
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n
t
h
e
l
o
w
-
f
r
e
que
nc
y
n
o
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s
e
e
m
i
s
s
i
o
n
.
T
h
e
r
a
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ge
(
)
f
o
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t
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n
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go
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t
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,
to
m
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in
s
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r
a
l
Evaluation Warning : The document was created with Spire.PDF for Python.
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5
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Step1: Initialize:
Initialize a population of sand cats with random positions
Evaluate the fitness of every sand cat
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Update the sand cat
’
s position using the following equation:
newPosition=current position + random() * (best position
-
current position)
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Step 4: Measure th
e fitness of the new position
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Update the sand cat
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Update global best position if applicable
Step 5: Return global best position
Step 6: End Procedure
3.
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Input:
WDBC and mammography dataset
Output:
Classify as Malignant or
benign
Classify as recurrence or non
-
recurrence
Begin
If the WDBC dataset performs:
Begin:
Perform data collection and pre
-
processing, then normalize the data.
Apply GA for feature extraction.
Build the model using SVM, KNN, RF, VC
Train the model
Feed testing data to models and get the results
Evaluation Warning : The document was created with Spire.PDF for Python.
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Construct the graphs for the obtained results.
End
elif mammography images:
Perform data collection and pre
-
processing, then normalize the data.
Apply CNN for feature extraction.
SCSO is applied for feature O
ptimization.
Build the model using SVM.
Train the model
Feed testing data to models and get the results
Construct the graphs for the obtained results.
End
End
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2023, pp. 1254
–
1259
,
d
oi
:
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C
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S
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S
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[
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, A
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–
1514
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om
put
in
g and R
e
ne
w
abl
e
Sy
s
te
m
s
(
I
C
A
C
R
S)
, I
E
E
E
, D
e
c
. 202
3, pp. 256
–
261
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do
i:
10.1109/
I
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A
C
R
S
58579.2023.10405212.
[
4]
M
.
S
.
K
uma
r
,
H
.
A
z
a
th
,
A
.
K
.
V
e
lm
ur
uga
n,
K
.
P
a
dma
na
ba
n,
a
nd
M
.
S
ubbi
a
h,
“
P
r
e
di
c
ti
o
n
of
A
l
z
h
e
im
e
r
’
s
di
s
e
a
s
e
us
in
g
h
ybr
id
ma
c
hi
ne
le
a
r
n
in
g t
e
c
hni
qu
e
,”
i
n
A
I
P
C
onf
e
r
e
nc
e
P
r
oc
e
e
di
ngs
,
2023, p. 020091
,
do
i:
10.1063/5.0110283
.
[
5]
R
.
R
a
ma
n,
K
.
D
hi
vy
a
,
P
.
S
a
pr
a
,
S
.
G
u
r
pur
,
S
.
P
.
M
a
ni
r
a
j,
a
n
d
S
.
M
ur
uga
n,
“
I
oT
-
dr
i
ve
n
s
ma
r
t
pa
c
ka
gi
ng
f
or
pha
r
ma
c
e
ut
i
c
a
ls
:
e
ns
ur
in
g
pr
o
du
c
t
in
te
g
r
it
y
a
nd
pa
ti
e
nt
s
a
f
e
t
y
,”
in
2023
I
nt
e
r
nat
io
nal
C
onf
e
r
e
nc
e
on
A
r
ti
f
ic
ia
l
I
nt
e
ll
ig
e
nc
e
f
or
I
nnov
at
io
n
s
in
H
e
al
th
c
ar
e
I
ndus
tr
i
e
s
(
I
C
A
I
I
H
I
)
,
I
E
E
E
, D
e
c
. 2023, pp. 1
–
6
,
d
o
i
:
10.1109/I
C
A
I
I
H
I
57871.2023.10489420.
[
6]
S
.
S
im
s
e
k,
U
.
K
ur
s
unc
u,
E
.
K
ib
is
,
M
.
A
ni
s
A
bde
ll
a
ti
f
,
a
nd
A
.
D
a
g,
“
A
hy
b
r
id
da
ta
mi
ni
ng
a
ppr
o
a
c
h
f
o
r
id
e
nt
i
f
y
in
g
th
e
t
e
mp
o
r
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l
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f
f
e
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ts
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r
ia
bl
e
s
a
s
s
o
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ia
t
e
d
w
it
h
br
e
a
s
t
c
a
nc
e
r
s
ur
v
i
v
a
l,
”
E
x
pe
r
t
Sy
s
te
m
s
w
it
h
A
ppl
ic
at
io
ns
,
v
o
l.
139,
p.
112863,
J
a
n.
2020,
do
i:
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s
w
a
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[
7]
P
.
E
.
J
e
ba
r
a
ni
,
“
G
r
e
y
w
o
l
f
o
pt
im
i
z
a
ti
o
n
ba
s
e
d
br
e
a
s
t
c
a
n
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e
r
d
e
t
e
c
ti
o
n
us
in
g
1D
c
o
n
vo
lu
ti
o
n
L
S
T
M
c
la
s
s
if
i
e
r
,”
P
r
z
e
gl
ąd
E
le
k
tr
ot
e
c
hni
c
z
ny
, v
o
l.
1, n
o
. 1,
pp. 3
–
11, J
a
n. 2023, do
i:
10.15
199/
48.2023.01.01.
[
8]
A
.
S
a
hu,
P
.
K
.
D
a
s
,
a
nd
S
.
M
e
he
r
,
“
H
ig
h
a
c
c
ur
a
c
y
h
y
br
id
C
N
N
c
la
s
s
if
ie
r
s
f
o
r
br
e
a
s
t
c
a
n
c
e
r
d
e
t
e
c
ti
o
n
us
in
g
ma
mm
o
gr
a
m
a
nd
ul
tr
a
s
o
und da
ta
s
e
ts
,”
B
io
m
e
di
c
al
Si
gnal
P
r
oc
e
s
s
in
g and C
ont
r
o
l
, vo
l.
80, F
e
b.
2023, d
o
i:
10.1016
/j
.bs
pc
.2022.104292.
[
9]
N
.
C
he
n,
B
.
H
a
n,
Z
.
L
i,
a
nd
H
.
W
a
ng,
“
B
r
e
a
s
t
c
a
n
c
e
r
pr
e
di
c
ti
o
n
ba
s
e
d
o
n
th
e
C
N
N
mo
d
e
ls
,”
H
ig
hl
ig
ht
s
in
Sc
ie
nc
e
,
E
ngi
ne
e
r
in
g
and T
e
c
hnol
ogy
, vo
l.
34, pp. 103
–
109, F
e
b. 2023, d
o
i:
10.5409
7/
hs
e
t.
v
34i
.5388.
[
10]
B
.
M
.
A
z
ma
n,
S
.
I
.
H
us
s
a
in
,
N
.
A
.
A
z
mi
,
M
.
Z
.
A
.
A
bd
G
ha
ni
,
a
nd
N
.
I
.
D
.
N
or
le
n,
“
P
r
e
di
c
ti
o
n
of
di
s
ta
nt
r
e
c
ur
r
e
n
c
e
in
br
e
a
s
t
c
a
nc
e
r
us
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g
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d
e
e
p
ne
u
r
a
l
n
e
tw
o
r
k,”
R
e
v
is
ta
I
nt
e
r
nac
io
nal
de
M
e
to
dos
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um
e
r
ic
o
s
par
a
C
al
c
ul
o
y
D
is
e
no
e
n
I
nge
ni
e
r
ia
,
vo
l.
38,
no
. 1, 202
2, d
o
i:
10.23967/j
.
r
im
ni
.2022.03.006.
[
11]
A
.
B
e
gum,
V
.
D
.
K
uma
r
,
J
.
A
s
gha
r
,
D
.
H
e
ma
la
th
a
,
a
nd
G
.
A
r
ul
kuma
r
a
n,
“
A
c
ombi
ne
d
d
e
e
p
C
N
N
:
L
S
T
M
w
it
h
a
r
a
nd
o
m
f
or
e
s
t
a
ppr
o
a
c
h
f
o
r
b
r
e
a
s
t
c
a
n
c
e
r
di
a
gn
o
s
is
,”
C
om
pl
e
x
it
y
, v
o
l.
2022, n
o
. 1, J
a
n. 2022, do
i
:
10.1155/2022
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299621.
[
12]
T
.
N
.
M
a
hbub,
M
.
A
.
Y
o
us
uf
,
a
nd
M
.
N
.
U
ddi
n,
“
A
mo
di
f
ie
d
C
N
N
a
nd
f
uz
z
y
A
H
P
ba
s
e
d
br
e
a
s
t
c
a
n
c
e
r
s
ta
ge
d
e
t
e
c
t
i
o
n
s
y
s
te
m,”
in
2022 I
nt
e
r
nat
io
nal
C
onf
e
r
e
nc
e
on A
dv
anc
e
m
e
nt
i
n E
le
c
tr
ic
al
and E
le
c
tr
oni
c
E
ngi
ne
e
r
in
g (
I
C
A
E
E
E
)
, I
E
E
E
, F
e
b. 2022, pp.
1
–
6
,
do
i:
10.1109/
I
C
A
E
E
E
54957.2022.9836541.
[
13]
C
.
B
.
G
o
n
c
a
l
v
e
s
,
J
.
R
.
S
o
u
z
a
,
a
nd
H
.
F
e
r
na
nd
e
s
,
“
C
N
N
o
pt
i
mi
z
a
ti
o
n
us
in
g
s
ur
r
o
ga
te
e
vo
lu
ti
o
na
r
y
a
lg
o
r
it
hm
f
o
r
br
e
a
s
t
c
a
nc
e
r
de
t
e
c
t
i
o
n
us
in
g
in
f
r
a
r
e
d
im
a
ge
s
,”
in
2022
I
E
E
E
35t
h
I
nt
e
r
nat
io
nal
Sy
m
pos
iu
m
on
C
om
put
er
-
B
as
e
d
M
e
di
c
al
Sy
s
t
e
m
s
(
C
B
M
S)
,
I
E
E
E
, J
ul
. 2022, pp. 84
–
89
,
do
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:
10.1109/C
B
M
S
55023.2022.00
022.
[
14]
D
.
A
.
Z
e
ba
r
i,
H
.
H
a
r
o
n,
D
.
M
.
S
ul
a
im
a
n,
Y
.
Y
us
of
f
,
a
nd
M
.
N
.
M
.
O
th
ma
n,
“
C
N
N
-
ba
s
e
d
de
e
p
tr
a
ns
f
e
r
l
e
a
r
ni
ng
a
ppr
o
a
c
h
f
or
de
t
e
c
t
in
g br
e
a
s
t
c
a
n
c
e
r
i
n ma
mm
o
g
r
a
m
im
a
g
e
s
,”
i
n
2022 I
E
E
E
10t
h C
onf
e
r
e
nc
e
on Sy
s
te
m
s
, P
r
oc
e
s
s
and
C
ont
r
ol
(
I
C
SP
C
)
,
I
E
E
E
,
D
e
c
. 2022, pp. 256
–
261
,
d
oi
:
10.1109/I
C
S
P
C
55597.2022.1000
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[
15]
S
.
R
uba
n,
M
.
M
o
o
s
a
J
a
be
e
r
,
a
nd
R
.
S
he
n
oy
B
a
s
ti
,
“
I
mpr
ov
is
in
g
br
e
a
s
t
c
a
nc
e
r
de
t
e
c
ti
o
n
us
in
g
C
N
N
,
V
G
G
a
n
d
S
S
D
a
lg
o
r
it
h
ms
,”
in
2022
T
hi
r
d
I
nt
e
r
nat
io
nal
C
onf
e
r
e
n
c
e
on
I
nt
e
ll
ig
e
nt
C
om
put
in
g
I
ns
tr
um
e
nt
at
io
n
and
C
ont
r
ol
T
e
c
hnol
ogi
e
s
(
I
C
I
C
I
C
T
)
,
I
E
E
E
,
A
ug. 2022, pp. 1772
–
1776
,
do
i:
10.1109/
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C
I
C
I
C
T
54557.2022.
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[
16]
E
.
A
.
M
o
ha
me
d,
T
.
G
a
b
e
r
,
O
.
K
a
r
a
m,
a
nd
E
.
A
.
R
a
s
he
d,
“
A
nove
l
C
N
N
p
oo
li
ng
la
y
e
r
f
or
br
e
a
s
t
c
a
nc
e
r
s
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nt
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ti
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n
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la
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s
if
i
c
a
ti
o
n
f
r
o
m t
h
e
r
m
o
gr
a
ms
,”
P
L
O
S O
N
E
,
vo
l.
17, n
o
. 10,
O
c
t.
2022, d
o
i:
10.1371/j
o
u
r
na
l.
po
n
e
.0276523.
[
17]
S
.
R
.
S
a
nna
s
i
C
h
a
kr
a
v
a
r
th
y
,
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.
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ha
r
a
ni
dha
r
a
n,
a
nd
H
.
R
a
ja
gur
u,
“
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ul
ti
-
de
e
p
C
N
N
ba
s
e
d
e
x
p
e
r
im
e
n
ta
ti
o
ns
f
or
e
a
r
l
y
di
a
gn
o
s
i
s
of
br
e
a
s
t
c
a
n
c
e
r
,”
I
E
T
E
J
our
nal
of
R
e
s
e
ar
c
h
, v
ol
. 69, n
o
. 10, pp. 7
326
–
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t.
2023, d
o
i:
10.1080/03772063.
2022.2028584.
[
18]
M
.
A
.
M
e
z
h
e
r
,
A
.
A
lt
a
mi
mi
,
a
nd
R
.
A
lt
a
mi
mi
,
“
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n
e
nha
n
c
e
d
ge
n
e
ti
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f
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ld
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g
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lg
o
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hm
f
o
r
pr
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ta
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br
e
a
s
t
c
a
n
c
e
r
d
e
t
e
c
ti
o
n,”
P
e
e
r
J
C
om
put
e
r
Sc
ie
nc
e
, v
ol
. 8, J
un. 2022, do
i:
10.7717/p
e
e
r
j
-
c
s
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[
19]
E
.
B
a
dr
,
S
.
A
lm
ot
a
ir
i,
M
.
A
.
S
a
la
m,
a
nd
H
.
A
hme
d,
“
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e
w
s
e
que
nt
ia
l
a
nd
pa
r
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ll
e
l
s
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o
r
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f
o
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di
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gn
o
s
is
,”
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le
x
andr
ia
E
ngi
ne
e
r
in
g
J
our
nal
,
vo
l.
61,
n
o
.
3,
pp.
2520
–
2534,
M
a
r
.
2022,
do
i:
10.1016/j
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e
j.
2021.07.024.
[
20]
P.
-
T
.
Y
a
ng,
W
.
-
S
.
W
u,
C
.
-
C
.
W
u,
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.
-
N
.
S
hi
h,
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.
-
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.
H
s
ie
h,
a
nd
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.
-
L
.
H
s
u,
“
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r
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t
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nc
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r
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ur
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e
mb
le
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th
o
ds
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nd c
o
s
t
-
s
e
ns
it
i
ve
l
e
a
r
ni
ng,”
O
pe
n M
e
di
c
in
e
, vo
l.
16,
no
. 1, pp. 754
–
768, M
a
y
2021, d
o
i
:
10.1515/m
e
d
-
2021
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0282.
[
21]
S
.
S
.
I
tt
a
nna
v
a
r
a
nd
R
.
H
.
H
a
v
a
ld
a
r
,
“
D
e
t
e
c
ti
o
n
of
br
e
a
s
t
c
a
n
c
e
r
us
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g
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e
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f
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f
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tu
r
e
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le
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ti
o
n
w
it
h
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e
n
e
ti
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a
lg
or
it
hm
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nd
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ne
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r
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l
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e
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o
r
k,”
D
is
tr
ib
ut
e
d
and
P
ar
al
le
l
D
at
abas
e
s
,
vo
l.
40,
n
o
.
4,
pp.
675
–
697,
D
e
c
.
2022,
d
o
i:
10.1007
/s
10619
-
021
-
07355
-
w.
[
22]
O
.
J
.
E
gw
o
m,
M
.
H
a
s
s
a
n,
J
.
J
.
T
a
ni
mu,
M
.
H
a
ma
da
,
a
nd
O
.
M
.
O
ga
r
,
“
A
n
L
D
A
–
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V
M
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io
M
e
dI
nf
or
m
at
ic
s
, v
o
l.
2, n
o
. 3, pp. 345
–
358,
J
un. 2022, do
i:
10.3390/bi
o
m
e
di
n
f
or
ma
ti
c
s
2030022.
[
23]
H
.
W
a
ng,
Y
.
L
i,
S
.
A
.
K
ha
n,
a
nd
Y
.
L
u
o
,
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e
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ti
o
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e
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A
r
ti
f
ic
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l
I
nt
e
ll
ig
e
nc
e
in
M
e
di
c
in
e
,
v
o
l.
110,
N
ov
.
2020,
do
i:
10.1016/j
.a
r
tm
e
d.2020.101977.
[
24]
T
.
L
o
ugh
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n
e
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al
.
,
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ly
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ic
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is
tr
y
,
v
o
l.
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n
o
.
6,
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837
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un.
2022,
do
i:
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c
028.
[
25]
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ul
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o
. 8
, pp. 511
-
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ps
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/d
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o
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1996.
[
26]
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.
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.
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l.
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o
.
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ov
.
2020,
do
i:
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ja
in
.v
6i
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. 7, pp. 102
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8
]
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,
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ul
.
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444
–
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,
do
i:
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C
I
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[
29
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11042
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