# Quantum spin Hall states in graphene interacting with WS2 or WSe2

T. P. Kaloni1<EMAIL_ADDRESS>+1-2049522900 L. Kou2 T.
Frauenheim2 U. Schwingenschlögl1,
udo.schwingenschlogl@kaust.edu.sa,+966-544700080 1Physical Science &
Engineering Division, KAUST, Thuwal 23955-6900, Kingdom of Saudi Arabia
2Bremen Center for computational Materials Science, University of Bremen, Am
Falturm 1, 28359, Bremen, Germany

###### Abstract

In the framework of first-principles calculations, we investigate the
structural and electronic properties of graphene in contact with as well as
sandwiched between WS2 and WSe2 monolayers. We report the modification of the
band characteristics due to the interaction at the interface and demonstrate
that the presence of the dichalcogenides results in quantum spin Hall states
in the absence of a magnetic field.

##  I Introduction

Research on graphene today appears to have reached a peak, mainly because the
material is rather difficult to be utilized in electronic devices due to
limitations in high quality mass production. Thus, interest is shifting to
hybrid systems with other two-dimensional materials, both for application
purposes and to create opportunities to better understand basic physical and
chemical phenomena geim ; geim1 . Heterostructures of semiconducting MoS2 and
graphene already have been demonstrated, showing potential particularly in
data storage Bertolazzi . Moreover, the joint two-dimensional nature of the
two components can be utilized for fabricating large scale flexible
nanoelectronic devices. For example, a new generation of field effect
transistors based on heterostructures of WS2 and graphene on transparent and
flexible substrates has shown a promising performance Georgiou .

Two-dimensional topological insulators in a quantum spin Hall (QSH) state have
metallic edges that sustain dissipationless current flow Bernevig ; Roth . For
graphene this effect has been predicted by Kane and Mele using an analytical
model kane . Since the energy gap of graphene is tiny ($\sim 10^{-3}$ meV) due
to the small intrinsic spin-orbit coupling (SOC) yao , experimental
observation of the QSH state is difficult. Various ideas promising an
enhancement have been put forward, all based on proximity to areas in that the
electrons are subject to strong SOC, including heavy atom deposition prx , H
adsorption Balakrishnan , and heterostructures with MoTe2 and Bi2Te3 arxiv .

Experimental realization of a QSH state in the topological insulator HgTe
Bernevig  has initiated efforts on other materials, in particular on graphene.
Recently, experiments have demonstrated that graphene in a strong magnetic
field supports also a QSH state, which is interesting for novel quantum
circuits Young . However, the experimental difficulties coming along with
large magnetic fields lau  call for alternative approaches. In this work we
propose hybrid structures that host a QSH state in the absence of a magnetic
field. Specifically, we employ density functional theory (including van der
Waals corrections) to study the following systems: (a) graphene on WS2 or WSe2
and (b) graphene sandwiched between two WS2 or WSe2 layers. Our results
prevail that the SOC in graphene can be enhanced such that a reasonable energy
gap is achieved at the K/K′ points with large band splitting. QSH states are
demonstrated for the sandwich systems.

##  II Computational details

The heterostructures are constructed by joining a $4\times 4\times 1$
supercell of graphene with $3\times 3\times 1$ supercells of the
dichalcogenides. A vacuum layer of at least 15 Å thickness guarantees that
there is no artificial interaction perpendicular to the two-dimensional system
due to the periodic boundary conditions. For the structural relaxation we
employ the Quantum-ESPRESSO code qe , using the generalized gradient
approximation (GGA), in order to include the van der Waals forces. The atomic
positions are optimized until all forces have converged to less than 0.005
eV/Å. The electronic structure calculations then are performed using the
Vienna Ab-initio Simulation Package vasp  both with pure GGA and including the
spin. A plane wave cutoff energy of 450 eV and a Monkhorst-Pack $16\times
16\times 1$ k-mesh are employed.

##  III Results and discussion

Figure 1: (a) Graphene in contact with one dichalcogenide monolayer. (b) Graphene sandwiched between two dichalcogenide monolayers. Table 1: Distance between the subsystems ($d$), binding energy per C atom (Eb), band gap (Eg), valance band splitting ($\Delta_{v}$), and conduction band splitting ($\Delta_{c}$). | GGA | GGA+SOC
---|---|---
System |  $d$ (Å) | Eb (meV) | Eg (meV) | Eg (meV) |  $\Delta_{v}$ (meV) |  $\Delta_{c}$ (meV)
Graphene/WS2 | 3.41 | 53 | 0.9 | 0.7 | 33 | 33
WS2/Graphene/WS2 | 3.40 | 51 | 0.1 | 1.1 | 92 | 99
Graphene/WSe2 | 3.42 | 54 | 3.6 | 0.9 | 145 | 132
WSe2/Graphene/WSe2 | 3.42 | 52 | 3.0 | 1.0 | 149 | 153

The systems under consideration are illustrated in Fig. 1. The lattice
parameters of the graphene (9.98 Å) and WS2 supercells (9.57 Å) give rise to a
lattice mismatch of 2.7%. The distance between the two subsystems converges to
$d=3.41$ Å, see Table I, which is a typical value for graphene on a
semiconducting substrate. We observe no modification of the C$-$C bond
lengths, whereas the W$-$S bond lengths (2.45 Å to 2.46 Å) are modified as
compared to pristine WS2 (2.39 Å yun ) because of the lattice mismatch. We
find $d=3.42$ Å for graphene on WSe2 with a C$-$C bond length of 1.42 Å, which
is the value of pristine graphene. The W$-$Se bond length (2.52 Å to 2.53 Å)
is almost the same as in pristine WSe2 (2.52 Å yun ). Moreover, we obtain
$d=3.40$ Å and $d=3.42$ Å for graphene sandwiched between WS2 and WSe2 layers,
respectively, whereas the other structural parameters are the same as for the
simple contacts.

Graphene is zero-gap semiconductor with very weak SOC Taroni . On the other
hand, WS2 is semiconductor with a direct band gap of 1.30-1.35 eV Braga . Its
structural and electronic properties have been studied extensively, see Ref.
Huang  and the references therein. WS2 is widely utilized for $n$/$p$-doped
field effect transistors, for example Matthaus . Experimentally, the synthesis
and characterization of graphene on WS2 has been reported in Ref. Georgiou .
The authors claim that a device based on this system can operate at room
temperature with good current modulating capacity. We show in Fig. 2(a) the
band structure obtained for graphene on WS2 without SOC, demonstrating some
interaction between the two subsystems, though without chemical bonding. The
binding energy

$E_{\rm binding}=E_{\rm graphene/WS_{2}}-E_{\rm WS_{2}}-E_{\rm graphene},$

where $E_{\rm graphene/WS_{2}}$, $E_{\rm WS_{2}}$, and $E_{\rm graphene}$ are
the total energies of the hybrid structure, WS2, and graphene, respectively,
amounts to 53 meV per C atom, in agreement with Ref. Zhang . Due to this weak
interaction, a small band gap of 0.9 meV is obtained, see the zoomed view in
Fig. 2(a), with degeneracy of the K and K′ points.

When the SOC is switched on in the calculation, the spin degeneracy of the
bands is lifted, resulting in valence and conduction band splittings of 33
meV, see Table I, with a small band gap of 0.7 meV, see the right hand side of
Fig. 2(a). A similar behavior has been observed for graphene on Bi2Te3 and
MoTe2 arxiv . The characteristic shape of the bands near the Fermi level
signifies a QSH phase (band inversion under preserved time-reversal symmetry),
similar to the observations in InAs/GaSb prl8 . It should be noted that the
bands above and below the Fermi level have opposite spin in agreement with the
signature of the QSH phase. A similar picture appears in germanene nanorods
embedded in fully H-passivated germanene Seixas  and in two-dimensional Sn
Duan , for which the gap can be strongly enhanced by the application of the
strain such that devices can operate at room temperature.

Figure 2: GGA and GGA+SOC band structures for (a) graphene on WS2 and (b)
graphene sandwiched between WS2 layers. Zoomed views of the K point are shown
to clarify the band splitting.

In Fig. 2(b) we present results for graphene sandwiched between WS2 layers. We
find a 0.1 meV band gap without SOC and one of 1.1 meV when the SOC is taken
into account. The shape of the band structure again reflects a QSH state.
Larger valence and conduction band splittings of 92 meV and 99 meV,
respectively, are achieved because two WS2 layers are attached to the
graphene, see Table 1, similar to the 70 meV band splitting of graphene in
contact with the (111) surface of BiFe3 (which is magnetic and thus hosts a
quantum anomalous Hall effect prl14 ).

Figure 3: GGA and GGA+SOC band structures for (a) graphene on WSe2 and (b)
graphene sandwiched between WSe2 layers. Zoomed views of the K point are shown
to clarify the band splitting.

We also study hybrid systems with monolayer WSe2, which is a semiconductor
with a band gap of 1.65 eV chang . The binding energy of graphene on WSe2
turns out to be 54 meV and thus is similar to the WS2 case. On the other hand,
the band gap of 3.6 meV, see Fig. 3(a), is about 5 times larger than for WS2,
reflecting the stronger SOC in WSe2. A comparison of the bulk properties of
WS2 and WSe2 can be found in Refs. amin ; Rama , for example. Again, according
to Fig. 3(a), the spin degeneracy at the K and K′ points is lifted under
inclusion of the SOC. The band structure qualitatively reflects the same
characteristics as demonstrated for WS2 in Fig. 2(a). However, the valence and
conduction band splittings are enhanced to 145 meV and 132 meV, respectively,
and so is the band gap (0.9 meV), see the zoomed view in Fig. 3(a). This
finding is in agreement with the fact that the spin splitting due to SOC is
about 50 meV larger in bulk WeS2 than in bulk WSe2 amin ; Rama . For graphene
sandwiched between WSe2 layers, we find a reduction of the band gap from 3.0
meV to 1.0 meV under SOC with enhanced valence and conduction band splittings
of 149 meV and 153 meV, respectively, compare Fig. 3(b) to Fig. 2(b), as to be
expected from our previous discussion. In the WSe2 systems the QSH effect
therefore is significantly more pronounced than in the WS2 systems.

##  IV Conclusion

Based on first-principles calculations, we have investigate the structural and
electronic properties of hybrid systems consisting of graphene and WS$2$ or
WSe2. Band gaps of few meV are obtained due to the interaction between the
component materials. Moreover, band inversion is found at the K/K′ points,
which indicates QSH states. By the preserved time-reversal symmetry together
with the enhancement of the effective SOC in graphene, these systems are able
to realize topological phases and therefore QSH states. Usually a strong
magnetic field is needed to achieve a QSH state in graphene Young , while we
propose systems in that a QSH state appears without magnetic field, which is a
great advantage.

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11institutetext: Key Laboratory of Quark and Lepton Physics (MOE) and
Institute of Particle Physics, Central China Normal University, Wuhan, Hubei
430079, China.

# Improved quark coalescence model for spin alignments of vector mesons

Xin-Li Sheng 11<EMAIL_ADDRESS>
###### Abstract

We propose an improved quark coalescence model for spin alignment of vector
mesons by spin density matrix with phase space dependence. Within this model
we propose an understanding of spin alignments of vector mesons $\phi$ and
$K^{*0}$ in the static limit: a large positive deviation of $\rho_{00}$ for
$\phi$ mesons from $1/3$ may come from the electric part of the vector $\phi$
field, while a negative deviation of $\rho_{00}$ for $K^{*0}$ mesons may come
from the electric part of vorticity fields. In the low-$p_{T}$ region,
$\rho_{00}$ for $K^{*0}$ mesons is proportional to $p_{T}^{2}$, which is
qualitatively agree with experimental results.

##  1 Introduction

In ultra-relativistic heavy-ion collisions, a huge orbital angular momentum
(OAM) can be generated in the direction perpendicular to the reaction plane.
During the evolution of the fireball, the OAM is transferred to the spin
polarization of quarks through the spin-orbit coupling in nonlocal scatterings
Liang:2004ph . The polarized quarks will then combine and form polarized
hadrons and mesons in the freezeout stage Liang:2004ph ; Liang:2004xn ;
Yang:2017sdk . The spin alignment of vector mesons can be measured through the
angular distribution of decay daughters Schilling:1969um . Here we report on
our recent paper Sheng:2020ghv , where an improved quark coalescence model in
phase space with spin degrees of freedom was proposed. We give a general
relation between the density matrix of vector mesons and that of quarks. Using
the spin polarization of quarks in vorticity and vector meson fields,
predicted by the Wigner function approach Yang:2017sdk , we derive spin
alignments of vector mesons $\phi$ and $K^{*0}$. Our work propose possible
explanations for the experimental results obtained by ALICE and STAR
collaborations ALICE:2019aid ; Kundu:2021lra ; Singha:2020qns . This work
should be tested by a detailed and comprehensive simulation of vorticity
tensor fields and vector meson fields in heavy ion collisions.

Notations and conventions.  We suppress the time dependence of all quantities
throughout this paper since we work at the formation time of hadrons. We use
boldfaced symbols to represent three-vectors, e.g., ${\bf x}$ or ${\bf p}$,
and use ${\bf x}_{i}$ with subscript $i=x,y,z$ for the three components. The
momentum integration measure is denoted using shorthand notation $[d^{3}{\bf
p}]\equiv d^{3}{\bf p}/(2\pi)^{3}$.

##  2 Spin density matrix and quark coalescence model in phase space

In Ref. Yang:2017sdk , a quark coalescence model is constructed based on the
spin density matrix in momentum space. However, we need to formulate a model
that includes space-time dependence because spin polarizations of quarks are
inhomogeneous in heavy ion collisions. We generalize the definition of density
operator by including off-diagonal elements in momentum space,

$\rho=\sum_{s}\int d^{3}{\bf x}\int[d^{3}{\bf p}]w(s,{\bf x},{\bf
p})\int[d^{3}{\bf q}]e^{-i{\bf x}\cdot{\bf q}}\left|s,{\bf p}+\frac{{\bf
q}}{2}\right\rangle\left\langle s,{\bf p}-\frac{{\bf q}}{2}\right|\,,$ (1)

where $\left|s,{\bf p}\right\rangle$ is the spin-momentum state of considered
particles. Here ${\bf x}$ is the conjugate position of ${\bf q}$, i.e., the
difference between momenta of state bases. Such a definition is consistent
with the wave-packet description for a quantum particle, wherein a particle
has finite momentum uncertainty and consists of plane waves with various
momenta. The weight function $w(s,{\bf x},{\bf p})$ is actually the Wigner
function, which is the probability of finding a particle (described by a wave
packet) with spin $s$, center position ${\bf x}$, and average momentum ${\bf
p}$. It can be obtained by projecting the density operator onto two states
with the same spin and different momenta

$w(s,{\bf x},{\bf p})=\int[d^{3}{\bf q}]e^{i{\bf q}\cdot{\bf x}}\left\langle
s,{\bf p}+\frac{{\bf q}}{2}\right|\rho\left|s,{\bf p}-\frac{{\bf
q}}{2}\right\rangle\,.$ (2)

For quarks with spin $1/2$, we parameterize the weight function as

$w(q|s,{\bf x},{\bf p})=\frac{1}{2}f_{q}({\bf x},{\bf p})\left[1+sP_{q}({\bf
x},{\bf p})\right]\,,$ (3)

where $f_{q}({\bf x},{\bf p})$ is the normalized quark distribution function,
$\int d^{3}{\bf x}\int d^{3}[{\bf p}]f_{q}({\bf x},{\bf p})=1$, and
$P_{q}({\bf x},{\bf p})$ denotes the average spin polarization at phase space
point $({\bf x},{\bf p})$.

We note that definitions of the density operator in Eq. (1) and the weight
function in Eq. (2) can be applied to any kind of particles, such as
quarks/antiquarks or vector mesons. Therefore spin density matrix elements for
mesons are obtained similar to Eq. (2) by putting the density matrix operator
between two meson states,

$\rho^{M}_{S_{z1},S_{z2}}({\bf x},{\bf p})=\int[d^{3}{\bf q}]e^{i{\bf
q}\cdot{\bf x}}\left\langle M;S,S_{z1};{\bf p}+\frac{\bf
q}{2}\right|\rho_{M}\left|M;S,S_{z2};{\bf p}-\frac{\bf q}{2}\right\rangle\,,$
(4)

where $M$ labels the flavor state of the meson, $S=1$ for a vector meson, and
$S_{z}=-1,0,1$ denote spin states in the spin quantization direction. We take
the approximation that the meson’s density matrix is given by the direct
product of density matrices of its constitute quark/antiquark,
$\rho_{M}=\rho_{q\bar{q}}\equiv\rho_{q}\otimes\rho_{\bar{q}}$. Putting
$\rho_{q\bar{q}}$ into Eq. (4), we derive

$\displaystyle\rho^{M}_{S_{z1},S_{z2}}({\bf x},{\bf p})=$ $\displaystyle\int
d^{3}{\bf x}_{b}[d^{3}{\bf p}_{b}][d^{3}{\bf q}_{b}]e^{-i{\bf q}_{b}\cdot{\bf
x}_{b}}\varphi_{M}^{*}\left({\bf p}_{b}+\frac{{\bf
q}_{b}}{2}\right)\varphi_{M}\left({\bf p}_{b}-\frac{{\bf q}_{b}}{2}\right)$
$\displaystyle\times\sum_{s_{1},s_{2}}w\left(q_{1}\big{|}s_{1},{\bf
x}+\frac{{\bf x}_{b}}{2},\frac{{\bf p}}{2}+{\bf
p}_{b}\right)w\left(\bar{q}_{2}\big{|}s_{2},{\bf x}-\frac{{\bf
x}_{b}}{2},\frac{{\bf p}}{2}-{\bf p}_{b}\right)$
$\displaystyle\times\left\langle
S,S_{z1}\,\big{|}\,s_{1},s_{2}\right\rangle\left\langle
s_{1},s_{2}\,\big{|}\,S,S_{z2}\right\rangle\,,$ (5)

where we have used the fact that the spin state in a meson’s wave function can
be decoupled from the momentum state. The combination of spin states is
controlled by the Clebsch-Gordan coefficients $\langle
S,S_{z1}\,|\,s_{1},s_{2}\rangle$. Meanwhile, the inner product between the
meson’s momentum state $\left|M;{\bf p}\right\rangle$ and the quark-
antiquark’s momentum state $\left|{\bf p}_{1},{\bf p}_{2}\right\rangle$ gives
the meson wave function $\varphi_{M}$,

$\left\langle{\bf p}_{1},{\bf p}_{2}\big{|}M;{\bf
p}\right\rangle=(2\pi)^{3}\delta^{(3)}\left({\bf p}_{1}+{\bf p}_{2}-{\bf
p}\right)\varphi_{M}\left(\frac{{\bf p}_{1}-{\bf p}_{2}}{2}\right)\,.$ (6)

The simplest choice of $\varphi_{M}({\bf k})$ is the Gaussian distribution
$\varphi_{M}({\bf k})=N_{M}\exp[-{\bf k}^{2}/(2a_{M}^{2})]$, where $a_{M}$ is
the average momentum width for the meson’s wave function. The pre-factor
$N_{M}$ is determined by the normalization condition $\int[d^{3}{\bf
k}]\varphi^{*}_{M}({\bf k})\varphi_{M}({\bf k})=1$.

##  3 Spin alignments of $\phi$ and $K^{\ast 0}$

In relativistic heavy ion collisions, there are different sources for spin
polarizations of massive fermions: vorticity fields, electromagnetic fields,
and mean fields of vector mesons. Here we only consider polarizations induced
by vorticity and vector meson fields since contributions from electromagnetic
fields are believed to be negligible Sheng:2019kmk . For quarks and
antiquarks, the polarization along the direction of global OAM, i.e., the
$y$-direction, are given by

$P^{y}_{q/\bar{q}}({\bf x},{\bf
p})=\frac{1}{2}\boldsymbol{\omega}_{y}\pm\frac{1}{2m_{q}}(\boldsymbol{\varepsilon}\times{\bf
p})_{y}\pm\frac{g_{V}}{2m_{q}T}{\bf B}_{y}^{V}+\frac{g_{V}}{2m_{q}E_{p}T}({\bf
E}_{V}\times{\bf p})_{y}\,,$ (7)

which are derived in the Wigner function approach Yang:2017sdk . We have
defined the three-vector of the electric part of the thermal vorticity tensor
as $\boldsymbol{\omega}=\nabla\times(\beta{\bf u})/2$, and the magnetic part
as $\boldsymbol{\varepsilon}=-[\partial_{t}(\beta{\bf u})+\nabla(\beta
u^{0})]/2$, where $\beta=1/T$ is the inverse temperature and
$u^{\mu}=(u^{0},{\bf u})$ is the flow velocity. The strength tensor
$F_{V}^{\mu\nu}$ of vector meson fields is also decomposed into the electric
part ${\bf E}^{V}_{i}=F_{V}^{i0}$ and the magnetic part ${\bf
B}^{V}_{i}=-\epsilon^{ijk}F_{V}^{jk}$ in analogy with normal electromagnetic
fields, with $i,j,k=x,y,z$. We use $g_{V}$ to represent the coupling constant
between quark/antiquark and vector meson fields in the quark-meson model
Sakurai:1960ju ; Zacchi:2015lwa . For light quarks, i.e., $u$ and $d$ quarks,
the major contributions are $\rho$ and $\omega$ meson fields, while $s$ quarks
are mainly polarized by $\phi$ meson fields.

Substituting the average polarization (7) for $s$ and $\bar{s}$ into Eqs. (3)
and (5), we obtain the average value of the normalized spin density matrix
element $\bar{\rho}_{00}\equiv\rho_{00}/(\rho_{-1,-1}+\rho_{00}+\rho_{11})$
(which is also called the spin alignment) for $\phi$ mesons,

$\displaystyle\left\langle\bar{\rho}^{\phi}_{00}({\bf x},{\bf
p})\right\rangle\approx$
$\displaystyle\frac{1}{3}-\frac{1}{9}\left\langle\boldsymbol{\omega}_{y}^{2}\right\rangle-\frac{1}{27m_{s}^{2}}\left(\left\langle\boldsymbol{\varepsilon}_{x}^{2}\right\rangle+\left\langle\boldsymbol{\varepsilon}_{z}^{2}\right\rangle\right)\left\langle{\bf
p}_{b}^{2}\right\rangle_{\phi}$
$\displaystyle+\frac{g_{\phi}^{2}}{9m_{s}^{2}}\left[\left\langle\left(\beta{\bf
B}_{y}^{\phi}\right)^{2}\right\rangle+\left\langle\left(\beta{\bf
E}_{z}^{\phi}\right)^{2}\right\rangle\left\langle\frac{{\bf
p}_{b,x}^{2}}{E_{p_{1}}E_{p_{2}}}\right\rangle_{\phi}+\left\langle\left(\beta{\bf
E}_{x}^{\phi}\right)^{2}\right\rangle\left\langle\frac{{\bf
p}_{b,z}^{2}}{E_{p_{1}}E_{p_{2}}}\right\rangle_{\phi}\right]$
$\displaystyle+\frac{1}{36m_{s}^{2}}\left[\left\langle\boldsymbol{\varepsilon}_{z}^{2}\right\rangle-
g_{\phi}^{2}\left\langle\left(\beta{\bf
E}_{z}^{\phi}\right)^{2}\right\rangle\left\langle\frac{1}{E_{p_{1}}E_{p_{2}}}\right\rangle_{\phi}\right]{\bf
p}_{x}^{2}$
$\displaystyle+\frac{1}{36m_{s}^{2}}\left[\left\langle\boldsymbol{\varepsilon}_{x}^{2}\right\rangle-
g_{\phi}^{2}\left\langle\left(\beta{\bf
E}_{x}^{\phi}\right)^{2}\right\rangle\left\langle\frac{1}{E_{p_{1}}E_{p_{2}}}\right\rangle_{\phi}\right]{\bf
p}_{z}^{2}\,,$ (8)

where ${\bf p}_{1}={\bf p}/2+{\bf p}_{b}$ and ${\bf p}_{2}={\bf p}/2-{\bf
p}_{b}$. Here the spin quantization direction is chosen as the $y$-direction.
We use $\langle\cdots\rangle$ to represent the average over generating
positions of $\phi$ mesons, while $\langle\cdots\rangle_{\phi}$ to represent
the average on the $\phi$ meson’s wave function. We have
$\langle\cdots\rangle_{\phi}\equiv 8\int d^{3}{\bf x}_{b}d^{3}{\bf
p}_{b}\exp\left(-{\bf p}_{b}^{2}/a_{M}^{2}-a_{M}^{2}{\bf
x}_{b}^{2}\right)(\cdots)$ if we take the Gaussian distribution approximation
for the meson wave function. We find that, in Eq. (8), all contributions
appear independently as positive or negative quantities. For nearly static
$\phi$ mesons with ${\bf p}\simeq 0$, vorticity fields, including
$\boldsymbol{\omega}$ and $\boldsymbol{\varepsilon}$, have negative
contributions, while vector meson fields ${\bf E}_{\phi}$ and ${\bf B}_{\phi}$
have positive contributions. We have argued in Ref. Sheng:2019kmk  that the
dominant contribution to $\bar{\rho}^{\phi}_{00}$ may possibly be from the
electric part ${\bf E}_{\phi}$, which results in the positive deviation from
$1/3$ for $\phi$ mesons’ spin alignment ALICE:2019aid ; Kundu:2021lra ;
Singha:2020qns .

Let us turn to the spin alignment of vector meson $K^{*0}$ with flavor
$(d\bar{s})$. We note that $d$ quarks will be polarized by $\rho$ and $\omega$
meson fields, i.e., $P^{y}_{d}$ depends on ${\bf E}_{\rho}$, ${\bf B}_{\rho}$,
${\bf E}_{\omega}$, and ${\bf B}_{\omega}$. On the other hand, $\bar{s}$ is
polarized by $\phi$ meson fields ${\bf E}_{\phi}$ and ${\bf B}_{\phi}$. We
take the approximation that different kinds of meson fields do not have large
correlation in space, therefore only quadratic terms survive in the spin
alignment of $K^{*0}$,

$\displaystyle\left\langle\bar{\rho}^{K^{*0}}_{00}({\bf x},{\bf
p})\right\rangle\approx$
$\displaystyle\frac{1}{3}-\frac{1}{9}\left\langle\boldsymbol{\omega}_{y}^{2}\right\rangle-\frac{1}{27m_{s}^{2}}\left(\left\langle\boldsymbol{\varepsilon}_{x}^{2}\right\rangle+\left\langle\boldsymbol{\varepsilon}_{z}^{2}\right\rangle\right)\left\langle{\bf
p}_{b}^{2}\right\rangle_{K^{*0}}$
$\displaystyle+\frac{1}{36m_{s}m_{d}}\left[\left\langle\boldsymbol{\varepsilon}_{z}^{2}\right\rangle{\bf
p}_{x}^{2}+\left\langle\boldsymbol{\varepsilon}_{x}^{2}\right\rangle{\bf
p}_{z}^{2}\right]\,.$ (9)

This result is obtained by substitute polarizations of $d$ and $\bar{s}$,
given by Eq. (7), into Eqs. (3) and (5). We see that contributions from vector
meson fields are absent in Eq. (9). The spin alignment of $K^{*0}$ is
dominated by vorticity fields and will be smaller than $1/3$ for nearly static
$K^{*0}$, which qualitatively agrees with experimental results obtained by
ALICE and STAR ALICE:2019aid ; Kundu:2021lra ; Singha:2020qns . We note that
the contribution from $\boldsymbol{\varepsilon}$ for
$\bar{\rho}_{00}^{K^{*0}}$ is amplified by about $2.1\sim 2.3$ compared to
that for $\bar{\rho}_{00}^{\phi}$. Therefore $\boldsymbol{\varepsilon}$ may
have a sizable magnitude of contribution for $\bar{\rho}_{00}^{K^{*0}}$, but
is less important for $\bar{\rho}_{00}^{\phi}$. We also notice that the spin
alignment of $K^{*0}$ is proportional to $p_{T}^{2}$ in the low-$p_{T}$
region, which seems to be agree with the $p_{T}$ dependence in Refs.
ALICE:2019aid ; Kundu:2021lra ; Singha:2020qns .

##  4 Summary

We have constructed an improved quark coalescence model based on the spin
density matrix in phase space with coordinate dependence. Spin alignments for
vector mesons $\phi$ and $K^{*0}$ are derived from spin polarizations for
quarks. We propose understandings of significant positive (negative)
deviations of $\bar{\rho}_{00}$ for $\phi$ ($K^{*0}$) mesons from $1/3$, which
should be tested by detailed simulations of vorticity and vector meson fields
in heavy ion collisions.

## acknowledgments

X.-L. S. is supported in part by the National Natural Science Foundation of
China (NSFC) under Grant Nos. 11935007, 11221504, 11861131009, 11890714 (a
sub-grant of 11890710), 12047528, and by the Fundamental Research Funds for
Central Universities in China by the UCB-CCNU Collaboration Grant.

## References

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  * (2) Z. T. Liang and X. N. Wang, Phys. Lett. B 629 (2005), 20-26.
  * (3) Y. G. Yang, R. H. Fang, Q. Wang and X. N. Wang, Phys. Rev. C 97 (2018) no.3, 034917.
  * (4) K. Schilling, P. Seyboth and G. E. Wolf, Nucl. Phys. B 15, 397-412 (1970) [erratum: Nucl. Phys. B 18, 332 (1970)].
  * (5) X. L. Sheng, Q. Wang and X. N. Wang, Phys. Rev. D 102 (2020) no.5, 056013.
  * (6) S. Acharya et al. [ALICE], Phys. Rev. Lett. 125, no.1, 012301 (2020).
  * (7) S. Singha [STAR], Nucl. Phys. A 1005, 121733 (2021).
  * (8) S. Kundu [ALICE], Nucl. Phys. A 1005, 121912 (2021).
  * (9) X. L. Sheng, L. Oliva and Q. Wang, Phys. Rev. D 101 (2020) no.9, 096005.
  * (10) J. J. Sakurai, Annals Phys. 11, 1-48 (1960).
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figure table

Identification of lung nodules CT scan using YOLOv5 based on convolution
neural network

#

Haytham Al Ewaidat ID Jordan University of Science and Technology, Faculty of
Applied Medical Sciences, Department of Allied Medical Sciences-Radiologic
Technology, Irbid, Jordan, 22110  Youness El Brag ID Abdelmalek Essaâdi
University of Science and Technology, Faculty of Multi-Disciplinary Larache,
Department of Computer Sciences, ksar el kebir , Morocco, 92150

###### Abstract

Purpose: The lung nodules localization in CT scan images is the most difficult
task due to the complexity of the arbitrariness of shape, size, and texture of
lung nodules. This is a challenge to be faced when coming to developing
different solutions to improve detection systems. the deep learning approach
showed promising results by using convolutional neural network (CNN),
especially for image recognition and it’s one of the most used algorithm in
computer vision.
Approach: we use (CNN) building blocks based on YOLOv5 (you only look once) to
learn the features representations for nodule detection labels, in this paper,
we introduce a method for detecting lung cancer localization. Chest X-rays and
low-dose computed tomography are also possible screening methods, When it
comes to recognizing nodules in radiography, computer-aided diagnostic (CAD)
system based on (CNN) have demonstrated their worth. One-stage detector YOLOv5
trained on 280 annotated CT SCAN from a public dataset LIDC-IDRI based on
segmented pulmonary nodules.
Results: we analyze the predictions performance of the lung nodule locations,
and demarcates the relevant CT scan regions. In lung nodule localization the
accuracy is measured as mean average precision (mAP). the mAP takes into
account how well the bounding boxes are fitting the labels as well as how
accurate the predicted classes for those bounding boxes, the accuracy we got
92.27% .
Conclusion: this study was to identify the nodule that were developing in the
lungs of the participants. It was difficult to find information on lung
nodules in medical literature,

###### keywords:

computer-aided diagnostic, deep learning, Convolutional Neural Networks ,Lung
Nodule

Correspondence author: Dr Haytham Al Ewaidat, Department of Allied Medical
Sciences-Radiologic Technology, Faculty of Applied Medical Sciences, Jordan
University of Science and Technology. PO Box 3030, Irbid 22110, Jordan Tel:
(+962)27201000-26939; Fax: (+962)27201087; E-mail<EMAIL_ADDRESS>

*Address all correspondence to Haytham Al Ewaidat<EMAIL_ADDRESS>
##  1 introduction

As far as noninvasive therapy and clinical assessment are concerned, medical
image analysis offers a tremendous advantage. X-rays, CTs, MRIs, and
ultrasounds are utilized to make precise diagnoses based on the obtained
restorative images. By using attractive fields, CT can capture pictures on
film in medical imaging. One-of-a-kind lung cancer is responsible for 1.61
million fatalities per year. Most of the cases of lung cancer in Indonesia are
observed in the MIoT centers. If the tumor is identified early, the survival
percentage is better then. It’s not an easy task to find lung cancer in its
early stages. Approximately 80% of cancer patients are diagnosed at the core
or accelerated phase of the disease. Lung cancer is the second most common
cancer among men and the tenth most common among women worldwide. After breast
and colorectal cancer, lung cancer is the thirdly most common cancer among
women. Features extraction in image processing is one of the simplest and most
efficient dimensionality reduction approaches. The non-invasive nature of CT
imaging is one of its most notable characteristics. It’s surprising to see
angles increasing when compared to other imaging modalities.

Computed tomography imaging is the best technique for examining lung
disorders. CT scans, on the other hand, have a high probability of false-
positive results and are associated with cancer-causing radiation exposure.
When compared to standard-dose CT, low-dose CT utilizes a lot less radiation
contact power. The findings reveal that the detection sensitivity of low-dose
and standard-dose CT images is not significantly different. A well know
database the National Lung Screening Trial database shows that cancer-related
fatalities were considerably decreased in the group that was subjected to low-
dose CT scans rather than chest radiography. The sensitivity of lung nodule
identification may be improved by the use of more detailed anatomical
information, and better image registration methods. As a result, the datasets
have grown enormously. Up to 500 segments/slice may be generated from a single
scan, depending on how thick the slice is. A single slice is examined by a
competent radiologist in 2–3.5 minutes. A radiologist’s workload keeps rising
while screening a Ct for the presence of a suspicious nodule. The detection
sensitivity of nodules is influenced by a variety of factors, including the
size, location, form, nearby structures, edges, and density, in addition to
the CT slice section thickness.

Only 68 percent of lung cancer nodules are properly identified when only one
radiologist doctor views the scan, and up to 82% of the time when two
radiologists check the scan, according to the study results. Early diagnosis
of malignant lung nodules by radiologists is a tough, time-consuming, and
laborious process in and of itself. The radiologist needs a lot of time to
carefully screen a large number of images, but this method is prone to mistake
when looking for microscopic nodules.

An aid for radiologists is required in this case to speed up readings, catch
any missing nodules, and enable improved localization. A primary goal of
computer-aided detection systems was to minimize radiologists’ labor and boost
the detection rate of nodules. Newer CAD systems, on the other hand, can
distinguish between benign, and malignant nodules, which is helpful in the
screening process. CAD systems frequently beat professional radiologists in
nodule identification and localization tasks because of recent breakthroughs
in deep learning models, particularly in image processing. CAD systems, on the
other hand, have an FP rate of 1–8.2 per scan and a detection range of
38–100%, according to different studies. As a result of their likeness to one
other, benign, and malignant nodule remain a difficult challenge to solve.

During the screening process, a variety of mistakes might occur. For example,
if a scan fails to capture or recognize a specific region of the lesion or
fails to distinguish between benign, and malignant lesions in a patient’s
body, the patient may be at risk of misdiagnosis. Most people die as a result
of misdiagnoses and delays in treatment because of these mistakes. In
radiology, over 4% of reports include diagnostic mistakes on a daily, and
about 30% of aberrant radiological diagnoses are ignored. Early-stage lung
nodules may be detected and classified more accurately using different
methodologies such as deep learning.

Lung nodule identification using deep learning with a specific methodology is
presented in this research. lung CT images, physiological symptoms, and
clinical indicators, the suggested approach reduces false-positive findings
and eventually prevents invasive procedures. YOLOv5 is used which has
convolutional networks were built to identify and classify nodules. For nodule
identification. Nodule identification and classification using the publicly
accessible data set LIDC-IDRI surpasses state-of-the-art deep learning
techniques. Using a variety of techniques, we were able to reduce the number
of false positives in the learning algorithm.

Lung nodule computer-aided detection (CAD) systems were originally developed
in the late 1980s, but the processing resources required for sophisticated
image analysis methods at the time made these efforts unattractive. For image
analysis, and decision support systems based on computers, the graphics
processing unit and convolutional neural networks revolutionized their
performance. Some of the most important lung nodule identification and
classification approaches have been suggested by researchers in deep learning
based medical images analysis models. For lung nodule classification, Yutong
Xie et al [1] . proposed a method that utilizes Texture, Shape, and Deep Model
learned Data at the choice level.

Nodule heterogeneity may be shown with the use of this algorithm’s GLCM-based
surface descriptor, Fourier-shape descriptor, and a DCNN. Based on CNNs
Chougrad et al [2]. studied the classification of breast cancer using a CAD
framework. Transfer learning, on the other hand, takes just a small number of
medical images to train a system. With the use of the transfer learning
approach, the CNNs were taught to their fullest potential. In terms of
accuracy, CNN came out on top with a score of 98.94 percent. Using the wavelet
transform and principal component analysis, Heba Mohsen et al[3] developed a
DNN classifier for brain tumor classification. A technique of regularized
linear discriminant analysis was developed in 2015 by Sharma et al [4], and it
used a regularization parameter to perform a standard cross-validation
methodology. An appropriate collection of characteristics is needed to
evaluate medical data for illness prediction. Several evolutionary algorithms
have been used to find the best possible traits. Gravitational search and
Elephant Herd optimizations have recently been used to choose the best
features [5]. Another deep learning-based model created by Kuruvilla, and
Gunavathi, K. in 2014, an ANN-based cancer classification for CT scans.
Development of the statistical model used to classify the data was completed.
Compared to feed-forward networks feed-forward backpropagation networks are
more accurate, according to research. Classifier accuracy may be improved even
more by using the skewness feature [6].

Lung cancer detection categorization is becoming more and more popular due to
the rapid advancement of pattern recognition and image processing methods.
Textural evaluation of thin-section CT images has been used in the literature
to help distinguish various obstructive lung disorders. Attenuation
distribution statistics, acquisition-length parameter, and co-occurrence
descriptor are all included in 13-dimensional vectors of local textures
information developed by Chabat et al[7]. A Bayesian classifier is used for
feature segmentation. These five scalar metrics, max, entropy, energy,
contrast, and homogeneity were recovered per each co-occurrence matrix to
minimize the feature vector’s dimensionality. The textural characteristics of
Solitary Pulmonary Nodules discovered by CT have been described and assessed
by Yanjie Zhu et al [8]. It took 300 generations for 67 characteristics to be
retrieved, however, only 25 features were picked. SVM-based classifiers are
used for classification. For Interstitial Lung Disease, Sang Cheol Park and
colleagues [9] used a genetic algorithm to identify the best picture
attributes (ILD). Hiram et al [10] used the frequency domain, and SVM with RBF
to classify lung nodule classifications. Solitary pulmonary nodules may be
automatically detected using an algorithm provided by Hong et al[11]. True
nodules are identified and labeled on original images using an SVM classifier.
The LIDC-IDRI images database was used by Antonio et al[12], to classify lung
nodules. Ecological taxonomic diversity and taxonomic distinctness indexes are
used for classification using SVM [13]. Results show a 98.11% accuracy rate.

The mesh grid region growth approach was used in CT to select and analyze just
the pixels that were most likely to be relevant to the diagnosis. The ILD
status of all unselected pixels was determined to be negative. To recognize
lung cancer cells, Zhi-Hua et al[14] presented Neural Ensemble-based Detection
(NED), which makes use of an artificial neural network ensemble. Using this
technology, it is possible to accurately identify cancer cells. An algorithm
developed by Hui Chen et al[15], uses a Neural Networks Ensemble to construct
the categorization of a lung nodule on a thin section CT image (NNE). A model
suggested by Aggarwal, Furquan, and Kalra [16] is characterized by normal lung
architecture by which segmentation is done using the best possible thresholds.
Geometric, statistical, and grey level properties are used to extract
features. Classification is done using LDA. The accuracy is 84%, the
sensitivity is 97%, and the specificity is 53%. An inference-based approach to
identify lung cancer nodules has been developed by Roy, Sirohi, and Patle
[17]. To improve contrast, this technique employs grey transformations using
an active contour model, the image is segmented. Training the classifier is
done by extracting features such as area, mean, major axis, and minor axis
length. Overall, the system’s accuracy rate is 94.12%. This approach has a
disadvantage in that it does not distinguish between benign cancers and those
that are malignant. Authors have used wavelet feature descriptors to classify
lung nodules [18]. One and two-level decompositions of wavelet transformations
are used in this example. A total of 19 characteristics are derived from each
wavelet sub-band. SVM is used to distinguish between CT images that include
malignant nodules and those that do not.

##  2 Material and Methods

In this section, we introduce our methods for Lung Nodules localization We use
a one-stage-method based on YOLOv5 detection , the methodology has been split
into the following Subsections to explain the whole process of our method .

###  2.1  Dataset

For this research, the dataset has been collected from LIDC-IDRI. In LIDC-IDRI
image collection, thoracic CT scans with marked-up annotated lesions are
included. For the development, training, and assessment of computer-assisted
diagnostic (CAD) approaches for the detection and diagnosis of lung cancer is
a worldwide web-accessible resource One example of a public-private
partnership founded on consensus-based decision-making is this collaboration
between the National Cancer Institute, the Foundation for the National
Institute of Health, and Food and Drug Administration (FDA), which was
spearheaded by NCI and supported by the FDA. This data collection, which
includes 239 Ct images for training and 41 images for validation. is a subset
of the original dataset. Some of the samples are given below in the following
Fig.1.

Figure 1: Samples from dataset LIDC-IDRI Lung Cancer

###  2.2  Pre-Processing Data

Real-world data tends to be fragmentary, noisy, and inconclusive. This may
lead to low-quality data collection, which in turn can lead to low-quality
models. Data Preprocessing offers procedures that may properly organize the
data for better comprehension in the deep learning process to solve these
challenges. Data Preprocessing steps that have been used in this research
study are given in the following Fig.2.

Figure 2: Preprocessing steps for images

###  2.3 Model architecture

As discussed in the introduction in this research YOLOv5 model is used for
feature extraction and detection of lung nodules in CT scans. Let have a brief
discussion about Yolov5 and its architecture.

####  2.3.1 YOLOv5 for lung nodules localization

the whole structure of Yolov4 Optimal speed and accuracy of object detection
[19] is shown in Fig.3 and YOLOv5 illustration representation shown in Fig.4.
the YOLO family of models consists of three main components to every single-
stage object detector, and YOLOv5 has its own three main modules

Figure 3: Overview of YOLOv5 building blocks model architecture

  1. (1)

Backbone Figure 3:it’s mostly used to extract the elements of the most
significant feature from the images that have been provided. Cross Stage
Partial Networks(CSP) is the backbone of YOLOv5’s feature extraction, which
uses them to extract an image’s most informative details

  2. (2)

Neck Figure 3: it used to create feature pyramids. Feature pyramids aid models
in generalizing successfully when it comes to object scaling. It aids in the
identification of the same item at various scales and dimensions. Feature
pyramids are quite valuable and can help models perform effectively on data
that has never been examined. It’s not only FPN, BiFPN, and PANet that are
used in feature pyramid models

  3. (3)

Head Figure 3: it has layers that generate predictions from anchor boxes on
features and generated final output vectors with probabilities, object classes
scores, and bounding boxes., YOLOv5 uses the following choices for training
[20]

Figure 4:  Model detection can be considered a regression problem. The image
is divided into S * S grids in which bounding boxes are predicted for each
grid cell, along with their confidence value

####  2.3.2 Training Model

During the training and validation process, a total of 270 CT Scan images are
used of which 239 CT Scans are used for training and 41 are used for
validations. For training, the Google Colab is used which is an online
platform for training models. Which provides 16GB GPU free for training. The
batch size was kept to 16 and the number of epochs was kept to 100. Splitting
of data can be seen in Fig.5 .

Figure 5:  Dataset Splitting Diagram CT Scan images

##  3 Results

the model had initial leverage to train faster and predict the location of
lung nodules and demarcates the relevant CT scan regions. before diving into
the analysis of the results is necessary to explain the statistical machine
learning knowledge behind those results, the explanations have been split into
the following Subsections to explain the whole analysis of the method we use.

###  3.1 Evaluation Metrics

In this section, we describe Charts of evaluation metrics that got from our
experiment. It is known to us that, in the computer Aide system, the main part
is detecting the object inside the image. Common metrics for measuring the
performance of classification algorithms such as YOLOv5 that are based on CNN
include, Recall, precision, F-score, mAP, PR_curve, F1_curve , IOU [21],
overlapping error, and boundary-based evaluation, the evaluation metrics we
used is the mean Average Precision (mAP) [22], the precision, and F1-Curve. We
will briefly explain them in the following part. According to the theory of
the statistical machine learning , precision is a two-category statistical
indicator whose formula is .

Precision : measures how accurate is our predictions was. the percentage of
our predictions are correct as shown in Fig.8,and following equation1.

$Precision=\frac{TP}{TP+FP}$ (1)

Recall: measures how much of the true bbox were correctly predicted as shown
in the following equation.2.

$Recall=\frac{TP}{TP+FN}$ (2)

moreover, it is necessary to know TP, FP, and FN in the localization Nodules
task.

  1. (1)

True positive (TP): IoU$>$[formula] (in this work, [formula] takes 0.2) the
number of Localization frames (the same Ground Truth is only calculated
once[23])

  2. (2)

False positive (FP): the number of check boxes for IoU$<=$[formula] or the
number of redundant check boxes that detect the same Ground Truth

  3. (3)

False negative (FN): the number of Ground Truths not detected

the IoU is a measures of the degree of overlap between two boundaries. We use
that to measure how much our predicted frame overlaps with the ground truth
(the actual ground frame) ,the IOU is shown with Fig.7 as follows, and the
formula is as following Fig.6:

Figure 6: Graphical representation of the Intersection over Union (IoU=0.2)
calculation on a narrow-band imaging. The light blue rectangle represents the
ground truth bounding box, while the red rectangle represents the model
prediction. The IoU is calculated by dividing the overlap area by the total
area of union

after getting familiar with these definitions of statistical learning
formulas, we introduce the mAP (mean Average Precision). The mAP compares the
ground-truth bounding box to the detected box and returns a score. The higher
the score, the more accurate the model is in its detections.
F1-score is defined as the harmonic average of precision and recall as shown
in figure 10(a):

$F1\\_Score=\frac{2*Precision*Recall}{Precision+Recall}$ (3)

(a) the Predicted location of a Single Nodule

(b) the Predicted location of tow Nodules in region

Figure 7: Example of output Results

###  3.2 Experiment’s Setting

the set Hyper-parameters of our fine tuning model are shown in Table 1, Our
experiment uses Pytorch framework deep learning on GPU Tesla K80 by Google
open Platform Colab-research .

Parameters |  |  |  Value
---|---|---|---
Batch size |  |  |  16
Image size |  |  |  416
Epoch |  |  |  145
Learning rate |  |  |  0.01
Optimizer |  |  |  SGD
Table 1: Parameters and their value.

###  3.3 Experiment’s Result and Analysis

To check the model’s predictions, and generalizations a few evaluation
parameters must be tracked during training and validation. There are several
criteria to keep in mind while evaluating a box loss, Precision, and recall
values. The variable box benefits from objectivity and categorization. Fig.9
shows all of the graphs that were used for this work. And Figure 10(a) shows
the F1 indicator training process for a single category that we want to be
detected. The F1 score tends to be 0 with increasing confidence . Training and
validation box losses are reduced Fig.9, suggesting that the model is sound
good. the mAP is the abbreviation of median accuracy performances. The high
number indicates that this parameter is correct 92.27% as shown blow in Fig.8.

(a) mean Average Precision Evaluation

(b) Precision Evaluation

Figure 8: the important Evaluation Metrics

(a) the training confidence of object presence loss

(b) the validation confidence of object presence loss

(c) the training bounding box regression loss

(d) the validation bounding box regression loss

Figure 9: Results of feature extraction training and validation

Precision is needed to determine how accurate the model forecasts are 92.82%
following the figure 8. Only excellent results may be achieved by using the
recall method. the model performance showed a good benefit of using Hyper-
parameter tuning to make better Learning from data samples and generalize good
knowledge from distribution can be seen the following Fig.9 . Due to the
importance of both precision and recall, there is a precision-recall curve the
shows the tradeoff between the precision and recall values for different
thresholds. This curve helps to select the best threshold to maximize both
metrics, tin the following Fig.10(b)

(a) F1 indicator training process for single category

(b) Precision — Recall Curve of the validation data

Figure 10: the important Evaluation Metrics

##  4 Discussion Ana Conclusion

This research examined at how an AI model can help readers detect viewable
lung cancer in Ct images. Residents identified more viewable lung cancer when
AI was being used as a second reader.In this research, the dataset has been
collected from LIDC-IDRI. In LIDC-IDRI image collection, thoracic CT scans
with marked-up annotated lesions are included. Yolov5 model is used for
feature extraction and detection of lung nodules in CT scans.During the
training and validation process, a total of 270 CT Scan images are used of
which 239 CT Scans are used for training and 41 are used for validations. In
this study, the model’s performance was assessed using accuracy, precision,
and recall. The accuracy metric indicates how well the model recognised both
positive and negative instances. The precision metric measures how well the
model predicts both negative and positive cases. The model’s high accuracy,
precision, and recall imply that it has a small error possibility. Our
findings imply that the AI technique assists low experienced individuals in
terms of recall while benefiting more-experienced audience in terms of
precision. Previous research has revealed that inexperienced readers are more
likely to overlook lung malignancies, particularly lesions with a limited
visibility score. In this research LIDC-IDRI dataset is used which have lung
nodules in it. The purpose of this study was to identify the nodule that were
developing in the lungs of the participants. It was difficult to find
information on lung nodules in medical literature. Research in the medical
field often use deep learning. Deep learning will be utilised to develop an
algorithm with the support of previous medical imaging research, according to
the findings of a literature review. Using over 270 CT images , we were able
to classify and identify nodules using a deep learning algorithm. Using
medical images analysis based on deep neural networks, this study found that
as much as 92.27% of cancer could be detected. Nodules on radiographs are
easier to see with its help. Using this technology in the future will help
treat illnesses including brain tumours and breast cancer.

##  5 Disclosures

The authors declare that they have no conflict of interest

##  6 Acknowledgments

We would like to thank our respectful research assistant Moath Alawaqla, for
his distinguished role of data collection.

##  7 Funding

This work supported by Jordan University of Science and Technology, Irbid-
Jordan,

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