I
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14
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ter
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h
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Ar
tific
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eu
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etwo
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k
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B
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t c
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c
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rticle
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CC B
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C
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r
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s
p
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A
uth
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r
:
Ad
ed
ay
o
Olu
k
ay
o
d
e
Ojo
Dep
ar
tm
en
t o
f
E
lectr
ical/E
lectr
o
n
ics an
d
C
o
m
p
u
ter
E
n
g
in
ee
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g
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C
o
lleg
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f
E
n
g
in
ee
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in
g
Af
e
B
ab
alo
la
Un
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er
s
ity
Ad
o
-
E
k
iti,
E
k
iti State,
Nig
er
ia
E
m
ail:
o
jo
ao
@
ab
u
ad
.
e
d
u
.
n
g
1.
I
NT
RO
D
UCT
I
O
N
B
r
ea
s
t
h
is
to
p
ath
o
lo
g
y
is
th
e
s
tu
d
y
o
f
b
r
ea
s
t
tis
s
u
e
m
icr
o
s
co
p
ically
to
d
etec
t
an
d
d
i
ag
n
o
s
e
a
n
y
ab
n
o
r
m
alities
o
r
d
is
ea
s
es
s
u
ch
as
b
r
ea
s
t
ca
n
ce
r
.
I
t
is
an
ess
en
tial
p
ar
t
o
f
b
r
ea
s
t
ca
n
ce
r
d
iag
n
o
s
is
an
d
tr
ea
tm
en
t
p
lan
n
in
g
.
B
r
ea
s
t
ca
n
ce
r
r
e
m
ain
s
o
n
e
o
f
th
e
m
ajo
r
d
is
ea
s
es
lead
in
g
to
t
h
e
d
ea
th
o
f
wo
m
e
n
g
lo
b
ally
[
1
]
–
[
4
]
.
B
r
ea
s
t c
an
ce
r
ca
n
b
e
d
iag
n
o
s
ed
th
r
o
u
g
h
th
e
in
itial p
alp
atio
n
d
etec
tio
n
an
d
r
eg
u
lar
ch
ec
k
s
u
s
in
g
th
e
u
ltra
s
o
u
n
d
im
ag
in
g
e
x
am
in
atio
n
s
.
T
h
e
b
i
o
p
s
y
o
f
t
h
e
b
r
ea
s
t
tis
s
u
e
is
th
en
p
e
r
f
o
r
m
ed
a
f
ter
t
h
e
d
iag
n
o
s
is
if
th
e
im
a
g
in
g
ex
am
in
atio
n
s
h
o
ws a
p
o
ten
tial f
o
r
m
alig
n
a
n
t tis
s
u
e
g
r
o
wth
.
T
h
ese
b
io
p
s
ies o
f
th
e
b
r
ea
s
t ti
s
s
u
es e
n
ab
le
d
o
cto
r
s
to
h
is
to
lo
g
ically
ev
alu
ate
th
e
tis
s
u
e’
s
m
icr
o
s
co
p
ic
s
tr
u
ctu
r
e
an
d
co
m
p
o
n
en
ts
[
5
]
,
[
6
]
.
I
n
s
o
m
e
ca
s
es,
th
e
n
u
m
b
er
o
f
s
u
ch
p
atien
ts
is
s
ig
n
if
ican
t
wh
ich
m
ak
es
th
e
d
ia
g
n
o
s
is
tim
e
co
n
s
u
m
in
g
an
d
co
m
p
licate
d
b
y
th
e
p
ath
o
l
o
g
is
t
v
ar
iab
ilit
y
.
I
t
h
as
th
er
ef
o
r
e
b
ec
o
m
e
im
p
o
r
ta
n
t
th
at
co
m
p
u
ter
a
id
ed
ass
is
tan
ce
b
e
m
ad
e
av
ailab
le
f
o
r
p
ath
o
lo
g
is
ts
to
m
ak
e
in
f
o
r
m
ed
d
ec
is
io
n
.
T
h
is
m
ak
es
th
em
co
n
ce
n
tr
ate
o
n
th
e
ca
s
es
th
at
a
r
e
m
o
s
t
s
u
s
p
icio
u
s
an
d
to
g
et
ar
o
u
n
d
s
u
b
jectiv
e
in
ter
p
r
etati
o
n
in
o
r
d
e
r
to
en
s
u
r
e
th
e
a
cc
u
r
ac
y
o
f
th
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test
ed
r
esu
lts
.
B
r
ea
s
t
ca
n
ce
r
i
s
ca
teg
o
r
ized
in
a
v
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r
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s
f
o
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s
tu
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ies
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p
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g
n
o
s
is
an
d
its
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en
esis
in
clu
d
in
g
b
y
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lo
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,
s
tag
e,
ag
e
at
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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tell
I
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8
9
3
8
P
erfo
r
ma
n
ce
a
n
a
lysi
s
o
f a
n
e
u
r
o
mo
d
el
fo
r
b
r
ea
s
t h
is
to
p
a
th
o
l
o
g
y
d
ec
is
io
n
…
(
A
d
ed
a
yo
Olu
ka
yo
d
e
Ojo
)
103
d
iag
n
o
s
is
,
an
d
ex
p
r
ess
io
n
o
f
t
u
m
o
r
m
ak
er
s
.
Fam
ily
h
is
to
r
y
i
s
an
o
th
er
r
is
k
f
ac
t
o
r
t
h
at
n
ee
d
s
to
b
e
co
n
s
id
er
ed
.
I
f
o
n
e
o
r
two
s
ib
lin
g
s
,
a
p
ar
en
t,
o
r
b
o
th
,
h
a
v
e
ca
n
ce
r
,
th
e
r
is
k
o
f
d
ev
elo
p
in
g
b
r
ea
s
t
ca
n
ce
r
d
o
u
b
les.
T
h
e
ag
e
at
wh
ich
th
e
af
f
ec
te
d
r
elativ
e
wa
s
d
iag
n
o
s
ed
with
th
e
d
is
ea
s
e
i
s
also
an
im
p
o
r
tan
t f
ac
to
r
.
T
h
er
e
is
h
u
g
e
p
r
o
m
is
e
f
o
r
t
h
e
u
s
e
o
f
ar
tific
ial
in
te
l
lig
en
ce
(
AI
)
i
n
a
clin
ical
s
ettin
g
,
f
r
o
m
th
e
au
to
m
atio
n
o
f
d
iag
n
o
s
tic
p
r
o
ce
d
u
r
es
to
th
er
ap
eu
tic
d
ec
is
io
n
-
m
ak
in
g
an
d
clin
ical
r
esear
c
h
.
T
h
e
in
f
o
r
m
atio
n
r
eq
u
ir
ed
is
p
r
o
v
id
e
d
f
r
o
m
n
u
m
er
o
u
s
s
o
u
r
ce
s
f
o
r
d
iag
n
o
s
is
an
d
th
er
a
p
y
,
in
clu
d
in
g
clin
ic
al
n
o
tes,
lab
o
r
ato
r
y
test
s
,
p
h
ar
m
ac
y
d
ata,
m
ed
ica
l
im
ag
in
g
,
a
n
d
g
en
o
m
ic
d
ata
.
T
h
e
m
icr
o
en
v
ir
o
n
m
e
n
t
p
lay
s
a
cr
u
cial
r
o
le
in
in
f
lu
en
cin
g
v
ar
io
u
s
asp
ec
ts
o
f
ca
n
ce
r
,
s
u
ch
as
p
r
o
teo
m
e
m
u
tatio
n
s
,
ce
l
lu
lar
ch
ar
ac
ter
is
tics
,
g
en
etic
co
m
p
lex
ity
,
co
n
n
ec
tio
n
s
b
etwe
en
tu
m
o
r
ce
lls
,
ca
n
ce
r
p
r
o
g
r
ess
io
n
,
an
d
tr
ea
tm
en
t
ef
f
ec
tiv
en
ess
.
T
o
ac
c
u
r
ately
ass
ess
th
ese
ir
r
eg
u
lar
ities
,
it
is
ess
en
tial
t
o
em
p
lo
y
s
en
s
itiv
e
an
d
p
r
ec
i
s
e
tech
n
iq
u
es
th
at
ca
n
ev
alu
a
te
m
u
ltip
le
f
e
atu
r
es
s
im
u
ltan
eo
u
s
ly
.
Ho
wev
er
,
ex
tr
ac
tin
g
n
u
m
er
o
u
s
v
is
u
al
an
d
m
o
r
p
h
o
m
etr
ic
f
ea
tu
r
es f
r
o
m
p
at
h
o
lo
g
ical
im
a
g
es is
cu
r
r
en
tly
r
estricte
d
b
y
s
lid
e
q
u
ality
p
r
o
b
lem
s
an
d
tu
m
o
r
h
eter
o
g
en
eity
.
Desp
ite
th
ese
lim
itatio
n
s
,
u
tili
zin
g
AI
-
b
ased
an
aly
s
is
ca
n
h
elp
ad
d
r
es
s
th
e
s
h
o
r
tco
m
in
g
s
o
f
s
u
b
j
ec
tiv
e
v
is
u
al
an
d
s
em
i
-
q
u
a
n
titativ
e
ev
alu
atio
n
s
b
y
p
ath
o
lo
g
is
ts
.
T
h
is
a
d
v
an
ce
d
a
p
p
r
o
ac
h
allo
ws
f
o
r
a
co
m
p
r
eh
e
n
s
iv
e
ex
am
in
atio
n
o
f
co
m
p
lex
tis
s
u
e
ar
ch
itectu
r
es,
lead
in
g
to
a
m
o
r
e
th
o
r
o
u
g
h
u
n
d
er
s
tan
d
i
n
g
o
f
ca
n
ce
r
p
ath
o
lo
g
y
[
7
]
–
[
1
6
]
.
I
n
o
r
d
e
r
wo
r
d
s
,
th
e
u
s
e
o
f
AI
tech
n
iq
u
es
im
p
r
o
v
es
d
ia
g
o
n
is
tic
ac
cu
r
ac
y
e
v
en
wh
e
n
im
p
e
r
f
ec
tio
n
s
in
s
lid
e
im
ag
e
q
u
alit
y
wo
u
ld
h
av
e
m
ad
e
d
o
in
g
s
o
ex
tr
em
el
y
d
if
f
icu
lt.
Fo
r
in
s
tan
ce
,
f
u
zz
y
l
o
g
ic
ca
n
b
e
u
s
ed
to
m
im
ic
h
u
m
an
ju
d
g
em
en
t
wh
ich
m
ak
es
th
e
clin
ician
s
c
o
m
p
r
e
h
en
d
t
h
e
class
if
icatio
n
p
r
o
ce
s
s
an
d
f
in
d
in
g
s
ea
s
ily
.
Oth
er
s
im
ilar
tech
n
iq
u
es
in
clu
d
e
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
alg
o
r
ith
m
,
co
n
v
o
lu
tio
n
a
l
n
eu
r
al
n
e
two
r
k
s
(
C
NNs),
an
d
o
th
er
v
ar
ian
ts
o
f
d
ee
p
lear
n
in
g
al
g
o
r
ith
m
s
.
All
th
ese
attem
p
t
to
lear
n
f
ea
tu
r
e
s
d
ir
ec
tly
f
r
o
m
th
e
r
aw
d
ata
with
o
u
t
th
e
r
eq
u
ir
e
m
en
t
f
o
r
s
p
ec
ialized
in
p
u
t f
r
o
m
p
at
h
o
lo
g
is
ts
[
4
]
,
[
7
]
,
[
1
1
]
,
[
1
3
]
,
[
1
5
]
.
Ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
(
ANN)
ar
e
in
ter
co
n
n
ec
tio
n
s
o
f
p
r
o
ce
s
s
in
g
u
n
its
k
n
o
wn
as
n
e
u
r
o
n
s
,
wh
ic
h
h
av
e
b
ee
n
s
u
cc
ess
f
u
lly
u
s
ed
f
o
r
d
ata
m
i
n
in
g
a
n
d
p
r
ed
ictio
n
p
r
o
b
le
m
s
in
a
v
a
r
iety
o
f
ap
p
l
icatio
n
s
[
1
7
]
–
[
2
1
]
.
B
ec
au
s
e
ANN
ca
n
s
im
u
ltan
eo
u
s
ly
p
r
o
ce
s
s
in
f
o
r
m
atio
n
an
d
m
ak
e
d
ec
is
io
n
s
,
th
ey
ca
n
o
v
er
co
m
e
o
t
h
er
wis
e
co
m
p
lex
p
r
o
b
lem
s
[
2
2
]
–
[
2
5
]
.
T
h
e
n
eu
r
al
n
etwo
r
k
s
'
ab
ilit
y
to
g
en
er
alize
f
r
o
m
th
e
in
p
u
t
d
ata
to
p
atter
n
s
alr
ea
d
y
ex
is
tin
g
in
th
e
d
ata
m
ak
es
th
e
m
p
ar
ticu
lar
ly
s
u
ited
f
o
r
p
atte
r
n
r
ec
o
g
n
ictio
n
an
d
class
if
icat
io
n
p
r
o
b
lem
s
.
T
h
is
p
ap
er
d
e
v
elo
p
s
an
AI
tech
n
iq
u
e
f
o
r
t
h
e
ea
r
ly
b
r
ea
s
t
ca
n
ce
r
d
etec
tio
n
-
a
to
o
l
t
h
at
h
as
th
e
p
o
ten
tial
o
f
ass
is
tin
g
p
h
y
s
ician
s
in
m
a
k
in
g
in
f
o
r
m
e
d
d
ec
is
io
n
s
ab
o
u
t
th
e
b
est
co
u
r
s
e
o
f
ac
tio
n
f
o
r
ea
ch
p
atien
t,
lead
in
g
to
m
o
r
e
s
u
cc
ess
f
u
l
o
u
tco
m
es.
W
ith
co
n
tin
u
ed
r
esear
ch
a
n
d
d
ev
elo
p
m
en
t,
th
is
n
eu
r
o
m
o
d
el
co
u
ld
p
lay
a
cr
itical
r
o
le
in
th
e
f
ig
h
t a
g
ai
n
s
t b
r
ea
s
t c
an
ce
r
.
2.
M
E
T
H
O
D
T
h
e
d
e
v
astatin
g
ef
f
ec
t
o
f
b
r
ea
s
t
ca
n
ce
r
-
a
p
r
ev
alen
t
f
o
r
m
o
f
ca
n
ce
r
an
d
th
e
lead
in
g
ca
u
s
e
o
f
ca
n
ce
r
-
r
elate
d
f
atalities
am
o
n
g
wo
m
en
,
ca
n
b
e
r
ed
u
ce
d
b
y
t
im
ely
an
d
ea
r
ly
d
etec
tio
n
.
T
h
i
s
d
etec
tio
n
is
o
f
ten
d
o
n
e
with
th
e
aid
o
f
an
aly
s
is
o
f
h
is
to
p
ath
o
lo
g
ical
i
m
ag
es
s
u
ch
as
th
e
o
n
e
s
h
o
wn
in
Fig
u
r
e
1
.
Alth
o
u
g
h
th
e
af
o
r
em
en
tio
n
ed
tec
h
n
iq
u
e
h
as
g
ain
ed
p
o
p
u
lar
ity
f
o
r
b
r
ea
s
t
ca
n
ce
r
d
etec
tio
n
,
th
e
tec
h
n
iq
u
e
s
till
s
u
f
f
er
s
f
r
o
m
b
ein
g
tim
e
-
co
n
s
u
m
in
g
a
n
d
lab
o
u
r
-
in
te
n
s
iv
e.
Ad
d
itio
n
ally
,
th
e
p
o
s
s
ib
ilit
y
o
f
th
e
ex
is
ten
ce
o
f
v
ar
iatio
n
s
b
etwe
en
d
if
f
er
en
t
o
b
eser
v
er
s
ev
en
u
n
d
er
s
am
e
co
n
d
itio
n
s
,
h
as
p
r
o
m
p
ted
th
e
ap
p
licatio
n
o
f
s
o
f
t
-
co
m
p
u
tin
g
an
d
in
tellig
en
ce
tech
n
iq
u
e
p
r
o
p
o
s
e
d
in
th
is
wo
r
k
.
T
h
e
in
teg
r
atio
n
o
f
well
-
tr
ain
ed
in
tellig
en
t
d
ec
i
s
io
n
s
u
p
p
o
r
t
s
y
s
tem
in
th
e
m
an
ag
e
m
en
t
p
r
o
ce
s
s
p
o
s
s
es
s
es
th
e
p
o
ten
tial
o
f
en
h
a
n
cin
g
th
e
q
u
ality
o
f
d
ec
is
io
n
m
ak
in
g
as
well
as
th
e
s
p
ee
d
th
er
eo
f
.
T
h
e
tech
n
iq
u
e
e
m
p
lo
y
ed
i
n
th
is
wo
r
k
in
cl
u
d
es
th
e
ex
tr
ac
tio
n
o
f
tr
ain
in
g
d
ata
f
r
o
m
b
r
ea
s
t
ca
n
ce
r
m
icr
o
s
co
p
y
im
a
g
es
o
f
d
is
tin
ct
g
r
o
u
p
s
in
clu
d
in
g
n
o
r
m
al
tis
s
u
e,
b
en
ig
n
lesi
o
n
,
in
s
itu
ca
r
ci
n
o
m
a,
an
d
in
v
asiv
e
ca
r
cin
o
m
a.
T
h
er
ea
f
ter
,
th
e
ac
q
u
ir
ed
d
ata
was
u
s
ed
to
tr
ai
n
a
ca
r
ef
u
lly
d
esig
n
ed
ANN
u
s
in
g
th
e
r
esil
ien
t
b
ac
k
p
r
o
p
ag
atio
n
alg
o
r
ith
m
.
2
.
1
.
T
he
ba
ck
pro
pa
g
a
t
io
n a
l
g
o
rit
hm
a
nd
pro
po
s
ed
enha
ncem
ent
ANN
ar
e
m
ain
ly
in
ter
c
o
n
n
ec
tio
n
o
f
p
r
o
ce
s
s
in
g
n
o
d
es
ca
ll
ed
n
e
u
r
o
n
,
wh
ic
h
h
a
v
e
in
te
r
c
o
n
n
ec
tin
g
weig
h
ts
th
at
ar
e
m
o
d
if
ied
ac
c
o
r
d
in
g
to
a
tr
ain
in
g
r
u
le.
T
h
e
p
e
r
f
o
r
m
a
n
ce
o
f
n
eu
r
al
n
etwo
r
k
s
th
er
ef
o
r
e
d
e
p
en
d
s
,
am
id
s
t
o
th
er
f
ac
to
r
s
,
o
n
th
e
c
h
o
s
en
tr
ain
in
g
alg
o
r
ith
m
o
r
weig
h
t
ad
a
p
tatio
n
m
ec
h
an
is
m
.
T
h
e
b
ac
k
p
r
o
p
a
g
atio
n
alg
o
r
ith
m
is
o
n
e
o
f
th
e
m
o
s
t
p
o
p
u
lar
l
y
u
tili
ze
d
lear
n
in
g
al
g
o
r
ith
m
s
wh
ich
h
as
th
u
s
f
ar
b
ee
n
em
p
lo
y
e
d
f
o
r
n
u
m
er
o
u
s
p
r
o
b
lem
s
.
T
h
e
f
o
llo
win
g
ar
e
t
h
e
s
tep
s
u
tili
ze
d
t
o
tr
ain
a
f
ee
d
f
o
r
war
d
ANN
with
t
h
e
b
ac
k
p
r
o
p
ag
atio
n
alg
o
r
ith
m
:
Step
0
: I
n
th
is
s
tag
e,
all
weig
h
ts
ar
e
s
et
to
ze
r
o
o
r
a
s
m
all
r
an
d
o
m
v
al
u
e.
Set w
eig
h
ts
to
ze
r
o
.
Step
1
: T
h
is
s
tep
iter
ates o
v
er
s
tep
s
2
th
r
o
u
g
h
9
u
n
til a
p
a
r
ticu
lar
co
n
d
itio
n
is
m
et.
Step
2
: T
h
is
p
h
ase
in
v
o
lv
es d
o
in
g
th
e
ca
lcu
latio
n
s
f
r
o
m
s
tep
s
3
to
8
u
s
in
g
th
e
t
r
ain
in
g
p
atter
n
s
.
Step
3
: T
h
e
u
n
it p
r
o
v
id
es in
p
u
t to
ea
ch
in
p
u
t n
eu
r
o
n
i
n
th
is
s
tep
.
(
Xi,
i=1
……n
)
.
Ste
p
4
:
I
n
th
is
s
t
ep
ea
ch
h
id
d
e
n
u
n
it
(
Z
j,
j=
1
.
.
.
.
.
.
.
n
)
r
e
ce
iv
es
wei
g
h
te
d
i
n
p
u
t
f
r
o
m
i
n
p
u
t la
y
e
r
a
n
d
s
u
m
s
it
,
a
p
p
l
y
ac
ti
v
ati
o
n
f
u
n
cti
o
n
,
g
e
n
e
r
a
tes
o
u
t
p
u
t
o
f
t
h
e
h
i
d
d
e
n
la
y
e
r
.
T
h
e
f
o
r
m
u
l
a
u
s
e
d
t
o
s
u
m
w
ei
g
h
t
i
n
p
u
t si
g
n
a
ls
is
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
5
:
1
02
-
1
08
104
=
0
+
∑
=
1
(
1
)
An
d
th
e
f
o
r
m
u
la
u
s
ed
to
f
in
d
o
u
tp
u
t
s
ig
n
als
o
f
h
id
d
en
la
y
er
is
=
(
)
wh
er
e
f
is
th
e
ac
tiv
atio
n
f
u
n
c
tio
n
.
Step
5
:
I
n
th
is
s
tep
ea
ch
o
u
tp
u
t
u
n
it
s
u
m
s
its
weig
h
ted
in
p
u
t
s
ig
n
als,
an
d
o
u
tp
u
t
s
ig
n
al
is
o
b
tain
ed
b
y
ap
p
ly
in
g
ac
tiv
atio
n
f
u
n
cti
o
n
o
n
it.
=
0
+
∑
=
1
(
2
)
A
n
d
th
e
f
o
r
m
u
la
u
s
ed
to
f
i
n
d
o
u
tp
u
t sig
n
als o
f
h
id
d
e
n
lay
er
is
:
=
(
)
(
3
)
Step
6
:
I
n
th
is
s
tep
ea
ch
o
u
tp
u
t
u
n
it
r
ec
eiv
es
a
tar
g
et
p
atter
n
co
r
r
esp
o
n
d
in
g
to
t
h
e
in
p
u
t
tr
a
in
in
g
p
atter
n
.
T
h
e
f
o
r
m
u
la
is
:
=
(
−
)
′
(
)
(
4
)
an
d
th
en
co
m
p
u
tes its
er
r
o
r
in
f
o
r
m
atio
n
ter
m
s
.
I
ts
f
o
r
m
u
la
is
:
Δ
=
(
5
)
Step
7
: th
e
er
r
o
r
at
h
id
d
en
lay
er
is
ca
lcu
lated
b
y
u
s
in
g
th
e
f
o
r
m
u
la
:
=
∑
=
1
(
6
)
ca
lcu
lates its
weig
h
t c
o
r
r
ec
tio
n
ter
m
an
d
v
elo
city
u
p
d
ate
u
s
in
g
:
=
′
(
)
(
7
)
∆
=
(
8
)
Step
8
:
Up
d
ate
th
e
weig
h
ts
f
r
o
m
h
id
d
en
lay
e
r
to
o
u
tp
u
t
lay
er
an
d
weig
h
ts
f
r
o
m
in
p
u
t
lay
er
to
h
id
d
en
lay
er
.
T
h
e
f
o
r
m
u
la
is
as:
(
)
=
(
)
+
∆
(
9
)
(
)
=
(
)
+
∆
(
1
0
)
Step
9
: in
th
is
s
tep
th
e
s
to
p
p
in
g
co
n
d
itio
n
is
test
ed
.
I
n
th
is
s
tu
d
y
,
th
e
b
ac
k
p
r
o
p
ag
at
io
n
n
eu
r
al
n
etwo
r
k
(
B
PNN
)
al
g
o
r
ith
m
i
n
tr
o
d
u
ce
s
f
iv
e
v
ar
iab
le
lear
n
in
g
r
ate
s
tr
ateg
ies.
T
h
ese
s
tr
ateg
ies
in
v
o
lv
e
s
elec
tin
g
d
if
f
er
en
t
lear
n
in
g
r
ate
v
alu
es
f
r
o
m
a
p
r
e
d
ef
in
ed
r
an
g
e.
T
h
e
lear
n
in
g
r
ate
s
ch
em
es
in
clu
d
e
m
ec
h
an
is
m
s
f
o
r
g
r
o
win
g
,
d
ec
r
ea
s
in
g
,
ch
ao
tic,
an
d
o
s
cillatin
g
r
ates
o
f
lear
n
in
g
.
T
o
ex
p
lo
r
e
th
e
b
eh
a
v
io
r
o
f
th
e
B
PNN
alg
o
r
ith
m
wh
en
co
m
b
in
in
g
m
u
ltip
le
n
atu
r
al
lear
n
i
n
g
r
ates,
th
is
wo
r
k
p
r
o
p
o
s
es
a
h
y
b
r
id
lear
n
in
g
r
at
e
s
ch
em
e.
T
h
is
s
ch
em
e
c
o
m
p
r
is
es
a
r
an
d
o
m
ize
d
p
o
o
l o
f
f
iv
e
ad
d
itio
n
al
lear
n
in
g
r
ates,
ea
ch
ex
h
ib
itin
g
d
is
tin
ct
b
eh
av
io
r
s
.
Ov
er
all,
th
is
wo
r
k
p
r
esen
ts
lear
n
in
g
m
ec
h
a
n
is
m
s
th
at
allo
w
f
o
r
v
ar
iab
le
ch
an
g
es in
th
e
lear
n
in
g
r
ate.
2
.
2
.
Descript
io
n o
f
a
rt
if
icia
l
neura
l net
wo
rk
s
T
h
is
s
ec
tio
n
d
escr
ib
es
th
e
f
ea
t
u
r
e,
a
r
ch
itectu
r
e
a
n
d
p
r
o
ce
d
u
r
es
tak
en
in
th
e
d
esig
n
o
f
t
h
e
ANN
.
T
h
e
n
eu
r
al
n
etwo
r
k
was
d
e
v
elo
p
e
d
,
tr
ain
e
d
a
n
d
test
ed
in
th
e
MA
T
L
AB
s
o
f
twar
e
co
m
p
u
tin
g
en
v
ir
o
n
m
en
t.
T
h
e
tr
ain
in
g
d
ataset,
alr
ea
d
y
s
ep
ar
ated
in
to
in
p
u
t a
n
d
tar
g
et
d
ata,
was lo
ad
e
d
.
T
h
ese
d
ata
wer
e
s
p
lit in
to
tr
ain
i
n
g
o
f
7
0
%
(
3
9
9
s
am
p
les
in
th
e
d
ataset)
,
v
a
lid
atio
n
1
5
%
(
8
5
s
am
p
les
in
th
e
d
ataset)
a
n
d
test
in
g
s
ets
1
5
%
(
8
5
s
am
p
les
in
th
e
d
ataset)
.
Fe
ed
f
o
r
war
d
n
et
f
u
n
ctio
n
was
u
s
ed
to
cr
ea
te
n
etwo
r
k
with
a
s
in
g
le
h
id
d
en
lay
er
o
f
1
0
n
eu
r
o
n
s
.
T
h
e
tr
ain
in
g
al
g
o
r
ith
m
(
r
esil
ien
t
b
ac
k
p
r
o
p
a
g
atio
n
)
was
th
er
ea
f
ter
s
p
ec
if
ied
.
Oth
er
tr
ain
in
g
p
ar
am
eter
s
wer
e
th
en
s
et
f
o
r
th
e
n
etwo
r
k
,
in
clu
d
in
g
th
e
n
u
m
b
er
o
f
ep
o
c
h
s
,
th
e
tr
ain
in
g
g
o
al,
th
e
m
i
n
im
u
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
P
erfo
r
ma
n
ce
a
n
a
lysi
s
o
f a
n
e
u
r
o
mo
d
el
fo
r
b
r
ea
s
t h
is
to
p
a
th
o
l
o
g
y
d
ec
is
io
n
…
(
A
d
ed
a
yo
Olu
ka
yo
d
e
Ojo
)
105
g
r
ad
ien
t,
a
n
d
th
e
m
ax
im
u
m
n
u
m
b
er
o
f
v
alid
atio
n
f
ailu
r
es.
Af
ter
tr
ain
in
g
th
e
n
etwo
r
k
,
it
was
test
ed
o
n
th
e
d
ataset
an
d
th
e
o
b
s
er
v
e
d
r
esu
lt wa
s
v
is
u
alize
d
.
Fig
u
r
e
2
s
h
o
ws
t
h
e
n
eu
r
al
n
e
two
r
k
ar
c
h
itectu
r
e
wh
ich
h
as
f
iv
e
in
p
u
ts
an
d
th
r
ee
o
u
tp
u
ts
u
s
in
g
th
e
d
atasets
f
o
r
th
e
b
r
ea
s
t
ca
n
ce
r
d
etec
tio
n
.
T
h
e
i
n
p
u
ts
in
cl
u
d
e
r
ad
iu
s
m
ea
n
,
tex
t
u
r
e
m
ea
n
,
p
e
r
im
eter
m
ea
n
,
a
r
ea
m
ea
n
,
an
d
s
m
o
o
t
h
n
ess
wh
il
e
th
e
o
u
tp
u
t
in
clu
d
e
co
n
ca
v
e
p
o
i
n
t,
s
y
m
m
etr
y
an
d
f
r
ac
tu
al
d
im
en
s
io
n
.
1
0
n
e
u
r
o
n
s
wer
e
u
s
ed
f
o
r
th
e
h
id
d
en
la
y
er
f
o
r
tr
ain
i
n
g
a
n
d
test
in
g
o
f
th
e
d
ata.
T
h
e
b
ac
k
p
r
o
p
ag
atio
n
alg
o
r
ith
m
was
em
p
lo
y
ed
i
n
u
p
d
atin
g
th
e
weig
h
t
elem
en
ts
o
f
th
e
n
eu
r
al
n
et
wo
r
k
ar
ch
itectu
r
e
b
ased
o
n
th
e
o
b
s
er
v
ed
e
r
r
o
r
.
T
h
e
b
ac
k
p
r
o
p
a
g
atio
n
alg
o
r
ith
m
h
elp
s
to
im
p
r
o
v
e
th
e
tr
ain
in
g
p
r
o
ce
s
s
b
y
em
p
lo
y
in
g
a
lear
n
in
g
r
ate
d
y
n
am
ically
.
T
h
e
p
er
f
o
r
m
an
ce
o
f
th
e
b
r
ea
s
t
ca
n
ce
r
d
etec
tio
n
m
o
d
el
tr
ain
e
d
u
s
in
g
th
is
alg
o
r
ith
m
wo
u
ld
d
ep
en
d
o
n
v
ar
i
o
u
s
f
ac
to
r
s
,
in
clu
d
in
g
th
e
q
u
ality
an
d
s
ize
o
f
th
e
d
ataset,
n
etwo
r
k
ar
c
h
itectu
r
e,
h
y
p
e
r
p
ar
am
et
er
s
ettin
g
s
,
an
d
th
e
ch
o
ice
o
f
t
h
e
v
ar
ia
b
le
lear
n
in
g
r
ate
m
eth
o
d
.
Fig
u
r
e
1
.
T
y
p
ical
im
ag
es sh
o
win
g
n
o
r
m
al,
b
en
i
g
n
,
in
s
itu
ca
r
cin
o
m
a,
a
n
d
in
v
asiv
e
ca
r
cin
o
m
a
[
4
]
Fig
u
r
e
2
.
T
h
e
ar
ch
itectu
r
e
o
f
t
h
e
n
eu
r
al
n
etwo
r
k
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
Af
ter
th
e
tr
ain
in
g
an
d
tes
tin
g
o
f
th
e
ANN
.
T
h
e
r
eg
r
ess
io
n
p
lo
t
p
r
o
v
i
d
ed
v
alu
ab
le
in
s
ig
h
t
s
in
to
th
e
r
elatio
n
s
h
ip
b
etwe
en
th
e
n
eu
r
a
l
n
etwo
r
k
o
u
tp
u
t
a
n
d
th
e
e
x
p
ec
ted
v
alu
es
as
ev
id
en
ce
d
i
n
Fig
u
r
e
3
.
W
e
o
b
s
er
v
ed
a
s
ig
n
if
ican
t
p
o
s
itiv
e
s
lo
p
e,
in
d
icatin
g
th
at
ce
r
tain
n
etwo
r
k
f
ea
tu
r
es
h
av
e
a
d
ir
ec
t
im
p
ac
t
o
n
th
e
d
etec
tio
n
o
f
in
tr
u
s
io
n
s
.
T
h
is
s
u
g
g
ests
th
at
as
th
ese
f
ea
tu
r
es
in
cr
ea
s
e,
th
e
lik
elih
o
o
d
o
f
ac
cu
r
ately
d
etec
tin
g
b
r
ea
s
t
ca
n
ce
r
also
in
cr
ea
s
es.
Ad
d
itio
n
ally
,
t
h
e
in
ter
ce
p
t
v
alu
e
p
r
o
v
id
e
d
a
n
esti
m
ate
o
f
th
e
d
etec
tio
n
lik
elih
o
o
d
wh
en
al
l
n
etwo
r
k
f
ea
t
u
r
es a
r
e
ze
r
o
.
Fig
u
r
e
4
r
ep
r
esen
ts
th
e
3
D
v
is
u
aliza
tio
n
o
f
s
elec
ted
s
ec
tio
n
o
f
d
ata
s
et
u
s
ed
in
p
r
ed
ictin
g
b
r
ea
s
t
ca
n
ce
r
d
etec
tio
n
.
T
h
is
p
ar
ticu
la
r
s
ec
tio
n
in
clu
d
es
th
e
s
u
r
f
ac
e
tex
tu
r
e
,
th
e
r
ad
iu
s
o
f
th
e
lo
b
es,
an
d
t
h
e
ar
ea
o
f
th
e
lo
b
es.
W
h
er
e
th
e
lo
b
es
r
ef
e
r
to
th
e
s
ec
tio
n
o
f
t
h
e
b
r
ea
s
t
th
at
co
n
ta
in
s
th
e
g
lan
d
u
la
r
tis
s
u
es.
Fro
m
th
e
p
lo
t,
it
ca
n
b
e
o
b
s
er
v
ed
th
at
t
h
e
ar
ea
is
u
n
if
o
r
m
f
o
r
a
r
an
g
e
o
f
r
a
d
iu
s
an
d
s
u
r
f
ac
e
tex
tu
r
e,
a
n
d
th
e
n
in
cr
ea
s
es
r
ap
id
ly
wh
en
th
e
r
ad
iu
s
o
f
th
e
l
o
b
es
in
cr
ea
s
es
a
b
o
v
e
a
ce
r
tain
s
ize.
B
y
v
is
u
ali
zin
g
th
is
p
lo
t,
th
e
p
atter
n
s
o
f
p
o
s
s
ib
le
b
r
ea
s
t
ca
n
ce
r
f
ea
tu
r
es c
an
ea
s
ily
b
e
id
e
n
tifie
d
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
5
:
1
02
-
1
08
106
Fig
u
r
e
3
.
T
h
e
r
eg
r
ess
io
n
p
lo
t f
o
r
b
r
ea
s
t c
an
ce
r
d
etec
tio
n
Fig
u
r
e
4
.
Ov
e
r
v
iew
o
f
s
elec
ted
v
ar
iab
les
T
h
is
wo
r
k
co
m
p
ar
es
d
i
f
f
er
en
t
alg
o
r
ith
m
s
f
o
r
tr
ain
i
n
g
th
e
n
e
u
r
al
n
etwo
r
k
f
o
r
b
r
ea
s
t
ca
n
ce
r
d
etec
tio
n
m
ain
ly
ar
tific
ial
n
eu
r
al
n
etw
o
r
k
tr
ain
e
d
u
s
in
g
t
h
e
r
esil
ien
t
b
ac
k
p
r
o
p
a
g
atio
n
alg
o
r
ith
m
(
R
P
_
ANN)
,
lay
er
s
en
s
itiv
ity
-
b
ased
ar
tific
ial
n
e
u
r
al
n
etwo
r
k
(
L
SB
_
ANN
)
,
L
ev
en
b
er
g
-
Ma
r
q
u
ar
d
t
ar
tific
i
al
n
eu
r
al
n
etwo
r
k
(
L
M_
ANN
)
,
an
d
g
r
ad
ien
t
d
e
s
ce
n
t
alg
o
r
ith
m
(
GDA_
A
NN
)
.
R
P_
ANN
ex
h
ib
its
ex
ce
p
tio
n
al
ac
cu
r
ac
y
,
with
9
8
.
6
4
%
t
r
ain
i
n
g
v
a
r
ian
ce
ac
co
u
n
ted
f
o
r
(
VAF)
an
d
9
7
.
7
7
%
v
alid
atio
n
VAF
as
s
h
o
wn
in
T
ab
le
1
.
I
t
d
em
o
n
s
tr
ates p
r
o
f
icien
c
y
in
le
ar
n
in
g
co
m
p
lex
p
atter
n
s
an
d
g
en
er
alizin
g
to
u
n
s
ee
n
d
ata,
m
a
k
in
g
it a
p
r
o
m
is
in
g
ca
n
d
id
ate
f
o
r
clin
ical
im
p
lem
e
n
tatio
n
.
L
SB
_
ANN
also
p
er
f
o
r
m
s
well,
with
9
7
.
8
2
%
tr
ain
in
g
VAF
an
d
9
7
.
6
5
%
v
alid
atio
n
VAF,
s
h
o
win
g
its
p
o
ten
tial
a
s
an
ef
f
ec
tiv
e
b
r
ea
s
t
ca
n
ce
r
d
etec
tio
n
to
o
l.
L
M_
ANN
s
h
o
ws
p
r
o
m
is
e
with
9
7
.
5
1
%
tr
ain
in
g
VAF
an
d
9
6
.
0
8
%
v
alid
atio
n
VAF,
b
u
t
its
r
elativ
ely
h
ig
h
er
r
o
o
t
m
ea
n
s
q
u
ar
ed
er
r
o
r
(
R
MSE
)
s
u
g
g
ests
r
o
o
m
f
o
r
i
m
p
r
o
v
e
m
en
t
in
p
r
ed
ictiv
e
ac
c
u
r
ac
y
.
GDA_
A
NN
p
er
f
o
r
m
s
l
o
west,
wi
th
9
6
.
3
3
%
tr
ain
in
g
VAF
an
d
9
6
.
2
4
%
v
ali
d
atio
n
VAF,
in
d
icatin
g
lim
itat
io
n
s
in
p
r
ed
ictiv
e
ac
c
u
r
ac
y
co
m
p
ar
ed
to
th
e
o
th
e
r
alg
o
r
ith
m
s
.
Ov
er
all,
d
ee
p
lear
n
in
g
-
b
ased
alg
o
r
ith
m
s
lik
e
R
P_
ANN
an
d
L
SB
_
AN
N
h
o
ld
p
o
te
n
tial
f
o
r
im
p
r
o
v
in
g
b
r
ea
s
t
ca
n
ce
r
d
iag
n
o
s
is
.
T
h
eir
h
i
g
h
VAF
v
alu
es
a
n
d
s
tr
o
n
g
co
r
r
elatio
n
s
d
em
o
n
s
tr
ate
th
eir
e
f
f
icac
y
in
ac
cu
r
ately
class
if
y
in
g
b
r
ea
s
t c
an
ce
r
s
u
b
ty
p
es f
r
o
m
m
icr
o
s
co
p
y
im
ag
es.
I
m
p
lem
en
tatio
n
o
f
th
ese
alg
o
r
ith
m
s
co
u
ld
lead
to
tim
ely
in
ter
v
en
ti
o
n
s
an
d
r
ed
u
ce
d
d
is
ea
s
e
b
u
r
d
en
o
n
p
atien
ts
an
d
h
ea
lt
h
ca
r
e
s
y
s
tem
s
.
Ho
wev
er
,
th
e
s
tu
d
y
ac
k
n
o
wled
g
es
ce
r
ta
in
lim
itatio
n
s
,
in
clu
d
in
g
th
e
n
ee
d
f
o
r
a
lar
g
er
d
ataset
an
d
r
ea
l
-
wo
r
ld
clin
ical
v
alid
atio
n
to
ass
ess
th
e
alg
o
r
it
h
m
s
'
ef
f
ec
tiv
en
ess
.
Fu
tu
r
e
r
esear
ch
an
d
d
ev
el
o
p
m
en
t
in
au
to
m
ated
b
r
ea
s
t
ca
n
ce
r
d
etec
tio
n
h
o
l
d
p
r
o
m
is
e
f
o
r
tr
a
n
s
f
o
r
m
ativ
e
im
p
ac
ts
o
n
g
lo
b
al
h
ea
lth
ca
r
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
P
erfo
r
ma
n
ce
a
n
a
lysi
s
o
f a
n
e
u
r
o
mo
d
el
fo
r
b
r
ea
s
t h
is
to
p
a
th
o
l
o
g
y
d
ec
is
io
n
…
(
A
d
ed
a
yo
Olu
ka
yo
d
e
Ojo
)
107
T
ab
le
1
. P
er
f
o
r
m
an
ce
co
m
p
a
r
is
o
n
s
o
f
s
ev
er
al
alg
o
r
ith
m
f
o
r
b
r
ea
s
t c
an
ce
r
d
etec
tio
n
N
e
t
w
o
r
k
Tr
a
i
n
i
n
g
Te
st
i
n
g
Tr
a
i
n
i
n
g
V
A
F
V
a
l
i
d
a
t
i
o
n
V
A
F
C
M
D
VAF
R
M
S
E
R
P
_
A
N
N
9
8
.
6
4
9
7
.
7
7
0
.
9841
9
6
.
2
6
4
.
3
8
G
D
A
_
A
N
N
9
6
.
3
3
9
6
.
2
4
0
.
9
6
6
6
9
4
.
0
9
7
.
9
8
LM
_
A
N
N
9
7
.
5
1
9
6
.
0
8
0
.
9
8
7
4
9
3
.
5
7
4
.
1
4
LSB_
A
N
N
9
7
.
8
2
9
7
.
6
5
0
.
9
8
7
7
9
7
.
8
1
3
.
9
7
4.
CO
NCLU
SI
O
N
I
n
th
is
s
tu
d
y
,
an
ANN
b
ased
n
eu
r
o
m
o
d
el
s
er
v
in
g
as
a
b
r
ea
s
t
h
is
to
p
ath
o
lo
g
y
d
ec
is
io
n
s
u
p
p
o
r
t
s
y
s
te
m
was
d
ev
elo
p
ed
.
T
h
e
an
al
y
s
is
o
f
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
n
e
u
r
o
m
o
d
el
s
h
o
ws
th
at
it
h
as
t
h
e
ab
ilit
y
to
p
r
o
v
id
e
v
alu
ab
le
s
u
p
p
o
r
t
in
a
n
aly
s
in
g
b
r
ea
s
t
h
is
to
p
ath
o
lo
g
y
s
am
p
les.
T
h
e
n
etwo
r
k
was
tr
ain
e
d
with
th
e
r
esil
ien
t
b
ac
k
p
r
o
p
ag
atio
n
alg
o
r
ith
m
u
s
in
g
d
ata
e
x
tr
ac
ted
f
r
o
m
clin
i
ca
l
s
o
u
r
ce
.
Fu
r
th
e
r
m
o
r
e,
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
tr
ain
ed
n
etwo
r
k
,
in
co
m
p
ar
is
o
n
with
o
th
er
n
etwo
r
k
s
with
s
im
ilar
p
ar
am
eter
s
,
in
d
icate
d
th
at
th
e
p
r
o
p
o
s
ed
n
eu
r
o
m
o
d
el
is
a
v
al
u
ab
le
t
o
o
l
i
n
th
e
m
an
a
g
em
en
t
p
r
o
ce
s
s
o
f
b
r
ea
s
t
ca
n
ce
r
.
T
h
is
wo
r
k
,
t
h
er
ef
o
r
e,
s
h
o
wca
s
es
th
e
ac
cu
r
ac
y
a
n
d
ap
p
licab
ilit
y
o
f
n
eu
r
o
m
o
d
els
in
m
ed
ical
d
iag
n
o
s
is
.
T
h
e
m
o
d
el,
wh
ic
h
is
ca
p
ab
le
o
f
id
en
tif
y
in
g
th
e
v
ar
io
u
s
p
atter
n
s
o
f
h
is
to
p
ath
o
lo
g
y
an
d
m
ak
in
g
r
elia
b
le
p
r
ed
ictio
n
s
,
r
ep
r
esen
ts
a
cr
iti
ca
l
d
ev
elo
p
m
en
t
in
b
r
ea
s
t c
an
ce
r
d
iag
n
o
s
is
an
d
tr
e
atm
en
t p
r
o
g
r
am
s
.
ACK
NO
WL
E
DG
E
M
E
NT
S
T
h
is
p
ap
er
is
f
in
an
ce
d
b
y
th
e
DAAD
Ni
R
eM
aS
Pro
ject
o
f
th
e
Un
iv
er
s
ity
o
f
C
o
lo
g
n
e
,
Ge
r
m
an
y
.
RE
F
E
R
E
NC
E
S
[
1
]
L.
B
e
n
o
i
t
e
t
a
l
.
,
“
A
d
v
e
r
se
o
u
t
c
o
me
p
a
t
h
w
a
y
f
r
o
m
a
c
t
i
v
a
t
i
o
n
o
f
t
h
e
A
h
R
t
o
b
r
e
a
st
c
a
n
c
e
r
-
r
e
l
a
t
e
d
d
e
a
t
h
,
”
E
n
v
i
r
o
n
m
e
n
t
I
n
t
e
r
n
a
t
i
o
n
a
l
,
v
o
l
.
1
6
5
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
n
v
i
n
t
.
2
0
2
2
.
1
0
7
3
2
3
.
[
2
]
Y
.
D
a
b
i
e
t
a
l
.
,
“
O
v
e
r
v
i
e
w
o
f
n
o
n
-
c
o
d
i
n
g
R
N
A
s
i
n
b
r
e
a
st
c
a
n
c
e
r
s,”
T
ra
n
sl
a
t
i
o
n
a
l
O
n
c
o
l
o
g
y
,
v
o
l
.
2
5
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
t
r
a
n
o
n
.
2
0
2
2
.
1
0
1
5
1
2
.
[
3
]
H
.
L
i
u
e
t
a
l
.
,
“
P
o
t
e
n
t
i
a
l
mo
l
e
c
u
l
a
r
mec
h
a
n
i
sms
a
n
d
c
l
i
n
i
c
a
l
p
r
o
g
r
e
ss
i
n
l
i
v
e
r
met
a
st
a
si
s
o
f
b
r
e
a
st
c
a
n
c
e
r
,
”
Bi
o
m
e
d
i
c
i
n
e
a
n
d
Ph
a
rm
a
c
o
t
h
e
r
a
p
y
,
v
o
l
.
1
4
9
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
b
i
o
p
h
a
.
2
0
2
2
.
1
1
2
8
2
4
.
[
4
]
Z.
H
a
mee
d
,
B
.
G
a
r
c
i
a
-
Za
p
i
r
a
i
n
,
J.
J
.
A
g
u
i
r
r
e
,
a
n
d
M
.
A
.
I
saz
a
-
R
u
g
e
t
,
“
M
u
l
t
i
c
l
a
ss
c
l
a
ss
i
f
i
c
a
t
i
o
n
o
f
b
r
e
a
s
t
c
a
n
c
e
r
h
i
st
o
p
a
t
h
o
l
o
g
y
i
ma
g
e
s
u
s
i
n
g
m
u
l
t
i
l
e
v
e
l
f
e
a
t
u
r
e
s
o
f
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
,
”
S
c
i
e
n
t
i
f
i
c
Re
p
o
r
t
s
,
v
o
l
.
1
2
,
n
o
.
1
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
3
8
/
s
4
1
5
9
8
-
0
2
2
-
1
9
2
7
8
-
2.
[
5
]
S
.
A
.
V
.
B
e
r
g
e
i
j
k
e
t
a
l
.
,
“
D
e
e
p
l
e
a
r
n
i
n
g
su
p
p
o
r
t
e
d
mi
t
o
ses
c
o
u
n
t
i
n
g
o
n
w
h
o
l
e
sl
i
d
e
i
ma
g
e
s:
A
p
i
l
o
t
st
u
d
y
f
o
r
v
a
l
i
d
a
t
i
n
g
b
r
e
a
st
c
a
n
c
e
r
g
r
a
d
i
n
g
i
n
t
h
e
c
l
i
n
i
c
a
l
w
o
r
k
f
l
o
w
,
”
J
o
u
rn
a
l
o
f
P
a
t
h
o
l
o
g
y
I
n
f
o
rm
a
t
i
c
s
,
v
o
l
.
1
4
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
j
p
i
.
2
0
2
3
.
1
0
0
3
1
6
.
[
6
]
X
.
Z
h
o
u
e
t
a
l
.
,
“
A
n
e
w
d
e
e
p
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
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o
f
O
b
ste
tri
c
s
a
n
d
G
y
n
a
e
c
o
lo
g
y
o
f
Ek
i
ti
S
tate
Un
iv
e
rsit
y
Tea
c
h
in
g
Ho
sp
it
a
l
(EK
S
UTH).
He
r
re
se
a
rc
h
in
tere
sts
a
re
i
n
t
h
e
a
re
a
o
f
m
a
tern
a
l
a
n
d
fe
tal
m
e
d
icin
e
,
p
re
e
c
lam
p
sia
a
n
d
Artif
icia
l
I
n
tel
li
g
e
n
c
e
(AI)
in
m
e
d
icin
e
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
lara
4
fa
v
o
u
r@
g
m
a
il
.
c
o
m
.
En
g
r
.
Dr
.
M
o
se
s
O
lu
wa
fe
m
i
O
n
ib
o
n
o
je
is
a
n
As
so
c
iate
P
ro
fe
ss
o
r
i
n
t
h
e
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l/
El
e
c
tro
n
ics
a
n
d
C
o
m
p
u
ter
En
g
in
e
e
rin
g
a
t
Afe
Ba
b
a
lo
la
Un
iv
e
rsit
y
Ad
o
-
E
k
it
i
(ABU
AD
),
Ni
g
e
ria.
H
e
sp
e
c
ialize
s
in
c
o
n
tr
o
l
a
n
d
in
str
u
m
e
n
tatio
n
e
n
g
in
e
e
rin
g
.
His
re
se
a
rc
h
a
re
a
s
in
c
lu
d
e
wire
les
s
se
n
so
r
n
e
two
r
k
s,
d
istri
b
u
ted
sy
ste
m
s
,
wire
les
s
in
stru
m
e
n
tat
io
n
s,
o
p
ti
m
iza
ti
o
n
m
e
t
h
o
d
s,
a
n
d
a
g
ro
-
e
lec
tro
in
str
u
m
e
n
tatio
n
s.
Dr
.
O
n
ib
o
n
o
je
h
o
l
d
s
a
P
h
.
D.
i
n
El
e
c
tro
n
ic
a
n
d
El
e
c
tri
c
a
l
E
n
g
i
n
e
e
rin
g
fr
o
m
Ob
a
fe
m
i
Aw
o
lo
w
o
Un
i
v
e
rsity
,
I
le
-
Ife
,
Nig
e
ria.
He
is
COREN
Re
g
istere
d
a
n
d
a
C
o
r
p
o
ra
te
M
e
m
b
e
r
o
f
t
h
e
Nig
e
ria
n
S
o
c
iety
o
f
En
g
in
e
e
rs.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
o
n
i
b
o
n
o
jem
o
@a
b
u
a
d
.
e
d
u
.
n
g
.
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