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Deep convolutional neural network (CNN) model optimization techniques—Review for medical imaging

  • The field of artificial intelligence (AI) and machine learning (ML) has been expanding and is explored by researchers in various fields. In medical diagnosis, for instance, the field of AI/ML is being explored because if medical diagnostic devices are built and designed with a backend of AI/ML, then the benefits would be unprecedented. Automated diagnostic tools would result in reduced health care costs, diagnosis without human intervention, overcoming human errors, and providing adequate and affordable medical care to a wider portion of the population with portions of the actual cost. One domain where AI/ML can make an immediate impact is medical imaging diagnosis (MID), namely the classification of medical images, where researchers have applied optimization techniques aiming to improve image classification accuracy. In this paper, we provide the research community with a comprehensive review of the most relevant studies to date on the use of deep CNN architecture optimization techniques for MID. As a case study, the application of these techniques to COVID-19 medical images were made. The impacts of the related variables, including datasets and AI/ML techniques, were investigated in detail. Additionally, the significant shortcomings and challenges of the techniques were touched upon. We concluded our work by affirming that the application of AI/ML techniques for MID will continue for many years to come, and the performance of the AI/ML classification techniques will continue to increase.

    Citation: Ghazanfar Latif, Jaafar Alghazo, Majid Ali Khan, Ghassen Ben Brahim, Khaled Fawagreh, Nazeeruddin Mohammad. Deep convolutional neural network (CNN) model optimization techniques—Review for medical imaging[J]. AIMS Mathematics, 2024, 9(8): 20539-20571. doi: 10.3934/math.2024998

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  • The field of artificial intelligence (AI) and machine learning (ML) has been expanding and is explored by researchers in various fields. In medical diagnosis, for instance, the field of AI/ML is being explored because if medical diagnostic devices are built and designed with a backend of AI/ML, then the benefits would be unprecedented. Automated diagnostic tools would result in reduced health care costs, diagnosis without human intervention, overcoming human errors, and providing adequate and affordable medical care to a wider portion of the population with portions of the actual cost. One domain where AI/ML can make an immediate impact is medical imaging diagnosis (MID), namely the classification of medical images, where researchers have applied optimization techniques aiming to improve image classification accuracy. In this paper, we provide the research community with a comprehensive review of the most relevant studies to date on the use of deep CNN architecture optimization techniques for MID. As a case study, the application of these techniques to COVID-19 medical images were made. The impacts of the related variables, including datasets and AI/ML techniques, were investigated in detail. Additionally, the significant shortcomings and challenges of the techniques were touched upon. We concluded our work by affirming that the application of AI/ML techniques for MID will continue for many years to come, and the performance of the AI/ML classification techniques will continue to increase.


    Any Lie group is equipped with a natural linear connection , and therefore, a canonical system of geodesic equations. This connection was introduced in 1926 by Cartan and Schouten [1]. Recently, a lot of work has been done on the symmetries of the geodesic equations of the canonical connection. Ghanam and Thompson considered the problem for all three and four-dimensional indecomposable Lie algebras [2]. They also considered six-dimensional nilpotent Lie algebras [3]. Almusawa et al. [4] considered the probelm for the five-dimensional indecomposable Lie algebras with co-dimension one abelian nilradical.

    Recently, Almutiben et al. considered the problem for the case where the nilradical is of co-dimension two. In dimension four, there is only one such indecompsable Lie algebra with co-dimensional two nilradical, namely, A4,12 in the Winternitz list [5]. In dimension five, there are three five-dimensional Lie algebras with co-dimension two abelian nilradical. These algebras are A5,33A5,35 in [5]. In all these cases, a comprehensive analysis of the symmetries of the geodesic equations was performed. Almutiben et al. [6] also considered the problem for the six-dimensional solvable indecomposable Lie algebras. Following the classification given by Turkowski [7], there are forty classes of non-isomorphic six-dimensional Lie algebras. Among these forty algebras, the first nineteen A6,1A6,19 have a four-dimensional, or equivalently co-dimension two, abelian nilradical and a two-dimensional abelian complement. Almutiben et al has given a comprehensive analysis of the symmetries in these nineteen cases [6].

    In this paper, we continue to study the symmetries corresponding to the eight algebras A6,20A6,27 in [7]. These algebras are characterized by the property that they have a four-dimensional abelian nilradical and a one-dimensional center.

    An outline of the paper is as follows: In Section 2, we provide some background material that helps to motivate our analysis. We do not give very specific details, but do provide some useful references. In Section 3, we give the definition of the canonical connection on a Lie group and a summary of its properties. In Section 4, we review the symmetries of differential equations and the Lie invariance condition. In Section 5, for each algebra A6,20A6,27 in Turkowski's list, we give the geodesic equations, a basis for the symmetry algebra in terms of vector fields, and, finally, we identify the symmetry Lie algebra in terms of the nilradical and its complement. We will use to denote a semi-direct product and for the direct sum of algebras.

    In order to motivate some of the material, we shall sketch a few of the key ideas encountered below. We shall be considering certain systems of second order ordinary differential equations. The space of independent variables that occur serve as a system of local coordinates on a Lie group G. We shall take for granted the basic definitions and properties of Lie groups. One may think of a Lie group as being an object that is intermediate between a vector space and a differentiable manifold. In particular, on a Lie group, one may make sense of various geometric objects (vector fields and one-forms primarily) as being left or right invariant. We refer the reader to [8,9,10] for readable introductions to the topic, that will be helpful in understanding the present article. In addition, these references help to explain the relationship between Lie groups and Lie algebras in a pragmatic way. Although the differential equations treated here technically "live" on a Lie group, in practice, all of our calculations are done at the Lie algebra level. Another more advanced source that covers the same material is [11].

    As regarding precise definitions related to Lie algebras, we refer in the first instance to [12] and also to [11]. For a solvable Lie algebra, one should think roughly of a subspace of upper triangular matrices and for a nilpotent Lie algebra, a subspace of the strictly upper triangular matrices. Nonetheless, abelian sub-algebras are nilpotent, so subspaces of diagonal matrices are also nilpotent.

    An important construct that we shall make use of is the semi-direct product of Lie algebras. The idea can be understood in various ways, but perhaps the simplest is to say that an algebra is a semi-direct product of Lie algebras if it is a vector space direct sum of a sub-algebra and an ideal. Solvable, not nilpotent, Lie algebras are only semi-direct products when there is an abelian complement to the nilradical. In this article, we shall be concerned with the Lie algebras A6,20A6,27 in [7]. Of these eight classes, only three, A6,22,A6,23,A6,27 for which ϵ=0, are semi-direct products. However, we shall see the appearance of semi-direct products again when we analyze the symmetry algebras in Section 5. In general, a symmetry algebra need not be solvable, but rather will have a Levi decomposition, that is, it will be a semi-direct product of a solvable ideal (that itself may or may not be a semi-direct product) and a semi-simple sub-algebra. All of the algebras A6,20A6,27 studied in Section 5, produce semi-simple sub-algebras.

    Concerning the definition of a linear connection, one may refer to [11] among a host of many excellent references. In relation to the current paper, one really only needs to understand that a linear connection produces a system of second order ordinary differential equations, the geodesics. These systems are similar to equations encountered in particle mechanics; the simplest example arises from the flat connection on Euclidean space (in arbitrary dimension), and the corresponding differential equations are the equations of motion of a free particle. More general connections introduce, as well as second order terms, first order terms that are quadratic in velocities.

    Finally, we come to the notion of symmetry of a differential equation. Lie's original idea was that a differential equation that could be integrated explicitly must possess an underlying symmetry. By the term "symmetry", we understand a change of variables may be both independent and dependent variables, such that after applying a finite transformation, the differential equation remains invariant. For a determined system of ordinary differential equations, and later, partial differential equations, the set of such symmetries comprises what was to become known as a Lie transformation group. Very quickly it was realized that the underlying structure need not be associated to a differential equation at all, and led to the idea of an abstract Lie group. It was also understood by Lie and his contemporaries, that it would be virtually impossible to calculate Lie transformation groups explicitly, even in some of the simplest cases. That circumstance led Lie to another great insight: that it would be far easier to work at the infinitesimal level and find not the Lie group, but rather its Lie algebra. In fact, Lie frequently uses the term "group", whereas today we would be more careful and refer to the "Lie algebra".

    In this work, Lie groups and Lie algebras appear at two levels. First of all, the differential equations that we study constitute an intrinsic part of the Lie group on which they are defined. Second, the set of symmetries of the differential equations itself forms a Lie group. However, the relationship between the two Lie groups and, more importantly, their associated Lie algebras is not a simple one in general. It is only in the case where the first Lie algebra has a trivial center that one can be sure that the first Lie algebra is isomorphic to a sub-algebra of the second; the sub-algebra in question is then either the algebra of left or right-invariant vector fields. In fact, the Lie algebras studied below in Section 5 all have a one-dimensional center.

    On left-invariant vector fields X and Y, the canonical symmetric connection on a Lie group G is defined by

    XY=12 [X,Y], (3.1)

    and then extended to arbitrary vector fields using linearity and the Leibnitz rule. The connection is left-invariant. One could just as well use right-invariant vector fields to define , but one must check that is well-defined. Properties of the canonical connection have been studied in [11], and we will summarize them in the following proposition:

    Proposition 1. For the canonical connection defined by (3.1):

    (1) The torsion is zero.

    (2) The curvature tensor R is given by R(X,Y)Z=14[[X,Y],Z].

    (3) The curvature tensor R is covariantly constant.

    (4) The curvature tensor R is zero if, and only if, the Lie algebra is two-step nilpotent.

    (5) The Ricci tensor is symmetric and in fact a multiple of the Killing form.

    (6) The Ricci tensor is bi-invariant.

    In this section, we explain the algorithm for finding the Lie symmetries of the geodesic equations. In local coordinates and in dimension n, the geodesic equations are given by

    d2xidt2+Γijkdxjdtdxkdt=0, (4.1)

    where Γijk are the connection components or Christoffel symbols, where i,j,k=1,...,n. In dimension six, let's take our coordinates to be t,p,q,x,y,z,w, where t is the independent variable and p,q,x,y,z,w are the dependant variables, so are functions of t. Define Γ to be

    Γ=Tt+Pp+Qq+Xx+Yy+Zz+Ww, (4.2)

    where T,P,Q,X,Y,Z, and W are unknown functions of (t,p,q,x,y,z,w). The first prolongation Γ1 and second prolongation Γ2 of Γ are given by

    Γ1=Γ+Pt˙p+Qt˙q+Xt˙x+Yt˙y+Zt˙z+Wt˙w, (4.3)
    Γ2=Γ1+Ptt¨p+Qtt¨q+Xtt¨x+Ytt¨y+Ztt¨z+Wtt¨w, (4.4)

    where

    Pt=Dt(P)˙pDt(T),Ptt=Dt(Pt)¨pDt(T),Qt=Dt(Q)˙qDt(T),Qtt=Dt(Qt)¨qDt(T),Xt=Dt(X)˙xDt(T),Xtt=Dt(Xt)¨xDt(T),Yt=Dt(Y)˙yDt(T),Ytt=Dt(Yt)¨yDt(T),Zt=Dt(Z)˙zDt(T),Ztt=Dt(Zt)¨zDt(T),Wt=Dt(W)˙wDt(T),Wtt=Dt(Wt)¨wDt(T), (4.5)

    and Dt is given by

    Dt=t+˙pp+˙qq+˙xx+˙yy+˙zz+˙ww+¨p˙p+¨q˙q+¨x˙x+¨y˙y+¨z˙z+¨w˙w. (4.6)

    Finally, Γ is said to be a Lie symmetry of the system of the geodesic equations if

    Γ2(Δ(2)i)|Δ(2)i=0=0, (4.7)

    where

    Δ(2)i=d2xidt2fi(t,xi),   i=1,2,...,6. (4.8)

    Equation (4.7) is called the Lie invariance condition. We equate the coefficients of the linearly independent derivation terms to zero, and this yields to an over-determined system of partial differential equations. For a good reference on symmetries of differential equations, we refer the reader to [13].

    In this section, we consider the eight six-dimensional Lie algebras with co-dimension two nilradical and one-dimensional center, A6,20A6,27 in [7]. For each Lie algebra, we will list the nonzero brackets, the system of the geodesic equations and, the symmetry vector fields. Finally, we analyze the symmetry Lie algebra in terms of its nilradical, complement, and semi-simple sub-algebra.

    The nonzero brackets for the algebra Aab6,20 are given by

    [e1,e4]=ae4,[e1,e6]=e6,[e2,e4]=be4,[e1,e2]=e3,[e2,e5]=e5. (5.1)

    The geodesic equations are given by

    ¨p=˙p(a˙z+b˙w),¨q=˙q˙z,¨x=˙x˙w,¨y=˙z˙w,¨z=0,¨w=0. (5.2)

    For the general case Aa0,b06,20, the symmetry Lie algebra is spanned by:

    e1=Dw,e2=Dz,e3=tDt,e4=Dt,e5=tDy,e6=Dp,e7=Dy,e8=Dq,e9=Dx,e10=pDp,e11=wDt,e12=zDt,e13=wDy,e14=zDy,e15=qDq,e16=xDx,e17=ezDq,e18=ewDx,e19=(wz2y)Dt,e20=(wz2y)Dy,e21=ebweazDp. (5.3)

    We make the following change of basis:

    ¯e1=e4,¯e2=e6,¯e3=e7,¯e4=e8,¯e5=e9,¯e6=e11,¯e7=e12,¯e8=e13,¯e9=e14,¯e10=e17,¯e11=e18,¯e12=e21,¯e13=e1+e142,¯e14=e2+e132,¯e15=e3e202,¯e16=e10,¯e17=e15,¯e18=e16,¯e19=e3+e202,¯e20=e5,¯e21=e19. (5.4)

    The nonzero brackets of the symmetry algebra are given by

    [e1,e15]=e1,[e1,e19]=e1,[e1,e20]=e3,[e2,e16]=e2,[e3,e15]=e3,[e3,e19]=e3,[e3,e21]=2e1,[e4,e17]=e4,[e5,e18]=e5,[e6,e13]=e1,[e6,e15]=e6,[e6,e19]=e6,[e6,e20]=e8,[e7,e14]=e1,[e7,e15]=e7,[e7,e19]=e7,[e7,e20]=e9,[e8,e13]=e3,[e8,e15]=e8,[e8,e19]=e8,[e8,e21]=2e6,[e9,e14]=e3,[e9,e15]=e9,[e9,e19]=e9,[e9,e21]=2e7,[e10,e14]=e10,[e10,e17]=e10,[e11,e13]=e11,[e11,e18]=e11,[e12,e13]=be12,[e12,e14]=ae12,[e12,e16]=e12,[e19,e20]=2e20,[e19,e21]=2e21,[e20,e21]=2e19. (5.5)

    We describe the symmetry algebra by the following proposition:

    Proposition 2. The symmetry Lie algebra is a twenty-one-dimensional Lie algebra. It is a semi-direct product of eighteen-dimensional solvable Lie algebra and sl(2, R ). The solvable part is ( R 12 R 6) a semi-direct product of  R 12 and  R 6. Therefore, the symmetry algebra can be identified as ( R 12 R 6)sl(2, R ).

    The nonzero brackets for the algebra Aa6,21 are given by

    [e1,e4]=e4,[e1,e5]=e6,[e2,e4]=ae4,[e2,e5]=e5,[e2,e6]=e6,[e1,e2]=e3. (5.6)

    The geodesic equations are given by

    ¨p=˙p(˙z+a˙w),¨q=˙w(˙qx˙z)+˙z˙x,¨x=˙x˙w,¨y=˙z˙w,¨z=0,¨w=0. (5.7)

    For the general case Aa06,21, the symmetry Lie algebra is spanned by

    e1=Dt,e2=tDy,e3=Dy,e4=Dp,e5=Dq,e6=Dz,e7=Dw,e8=tDt,e9=pDp,e10=wDt,e11=zDt,e12=wDy,e13=zDy,e14=xDq,e15=zDq+Dx,e16=qDq+xDx,e17=ewDq,e18=ewDx,e19=(wz2y)Dt,e20=(wz2y)Dy,e21=eawezDp. (5.8)

    We make the following change of basis:

    ¯e1=e1,¯e2=e3,¯e3=e4,¯e4=e5,¯e5=e10,¯e6=e11,¯e7=e12,¯e8=e13,¯e9=e14,¯e10=e15,¯e11=e17,¯e12=e18,¯e13=e21,¯e14=e6+e212,¯e15=e7+e132,¯e16=e8e202,¯e17=e9,¯e18=e16,¯e19=e2,¯e20=e8+e202,¯e21=e19. (5.9)

    The nonzero brackets of the symmetry algebra are given by:

    [e1,e15]=e1,[e1,e18]=e1,[e2,e18]=e2,[e3,e5]=e1,[e3,e15]=e3,[e3,e18]=e3,[e4,e5]=e2,[e4,e14]=e2,[e4,e18]=e4,[e6,e14]=e9,[e6,e16]=e6,[e6,e19]=e8,[e6,e20]=e6,[e7,e15]=e10,[e7,e16]=e7,[e7,e20]=e7,[e7,e21]=2e12,[e8,e14]=e10,[e8,e16]=e8,[e8,e20]=e8,[e8,e21]=2e6,[e9,e16]=e9,[e9,e19]=e10,[e9,e20]=e9,[e10,e16]=e10,[e10,e20]=e10,[e10,e21]=2e9,[e11,e17]=e11,[e12,e15]=e9,[e12,e16]=e12,[e12,e19]=e7,[e12,e20]=e12,[e13,e14]=e13,[e13,e15]=ae13,[e13,e17]=e13,[e19,e20]=2e19,[e19,e21]=2e20,[e20,e21]=2e21. (5.10)

    We describe the symmetry algebra by the following proposition:

    Proposition 3. The symmetry Lie algebra is a twenty-one-dimensional Lie algebra. It is a semi-direct product of eighteen-dimensional solvable Lie algebra and sl(2, R ). The nilradical is thirteen-dimensional decomposable Lie algebra. In fact, the nilradical is a direct sum of A5,1 in Winternitz [5] and  R 8. The nilradical has a five-dimensional abelian complement. Therefore, the symmetry algebra can be identified as

    ((A5,1R8)R5)sl(2,R),

    where the nonzero brackets of A5.1 are given by

    [e3,e5]=e1,[e4,e5]=e2. (5.11)

    The nonzero brackets for the algebra Aaϵ6,22 are given by

    [e1,e3]=e3,[e1,e5]=e6,[e2,e4]=e4,[e2,e3]=ae3,[e1,e2]=ϵe5. (5.12)

    The geodesic equations are given by

    ¨p=˙z˙y,¨q=˙w˙q,¨x=˙x(˙z+a˙w),¨y=0,¨z=0,¨w=0. (5.13)

    For the general case Aa0,ϵ=06,22, the symmetry Lie algebra is spanned by

    e1=Dy,e2=Dw,e3=Dz,e4=tDt,e5=Dx,e6=Dt,e7=tDp,e8=Dp,e9=Dq,e10=xDx,e11=wDt,e12=yDt,e13=zDt,e14=wDp,e15=yDp,e16=zDp,e17=qDq,e18=pDp+yDy,e19=ewDq,e20=z22Dp+zDy,e21=zt2Dp+tDy,e22=(yz2p)Dt,e23=(yz2p)Dp,e24=wz2Dp+wDy,e25=eawezDx,e26=(yz22pz)Dp+(yz2p)Dy. (5.14)

    We consider the following change of basis:

    ¯e1=e1,¯e2=e5,¯e3=e6,¯e4=e8,¯e5=e9,¯e6=e11,¯e7=e13,¯e8=e14,¯e9=e16,¯e10=e19,¯e11=e20,¯e12=e24,¯e13=e25,¯e14=e2,¯e15 =e3+e152,¯e16=e4+e18,¯e17=e10,¯e18=e17,¯e19=e4+e232,¯e20=e7,¯e21=e12,¯e22=e15,¯e23=e18+e23,¯e24=e21,¯e25=e22,¯e26=e26. (5.15)

    The nonzero brackets of the symmetry algebra are given by

    [e1,e15]=e42,[e1,e16]=e1,[e1,e19]=e92,[e1,e21]=e3,[e1,e22]=e4,[e1,e23]=e1+e9,[e1,e25]=e7,[e1,e26]=e11,[e2,e17]=e2,[e3,e16]=e3,[e3,e19]=e3,[e3,e20]=e4,[e3,e24]=e1+e92,[e4,e16]=e4,[e4,e19]=e4,[e4,e23]=e4,[e4,e25]=2e3,[e4,e26]=2e1e9,[e5,e18]=e5,[e6,e14]=e3,[e6,e16]=e6,[e6,e19]=e6,[e6,e20]=e8,[e6,e24]=e12,[e7,e15]=e3,[e7,e16]=e7,[e7,e19]=e7,[e7,e20]=e9,[e7,e24]=e11,[e8,e14]=e4,[e8,e16]=e8,[e8,e19]=e8,[e8,e23]=e8,[e8,e25]=2e6,[e8,e26]=2e12,[e9,e15]=e4,[e9,e16]=e9,[e9,e19]=e9,[e9,e23]=e9,[e9,e25]=2e7,[e9,e26]=2e11,[e10,e14]=e10,[e10,e18]=e10,[e11,e15]=e192,[e11,e16]=e11,[e11,e21]=e7,[e11,e22]=e9,[e11,e23]=e11,[e12,e14]=e192,[e12,e16]=e12,[e12,e21]=e6,[e12,e22]=e8,[e12,e23]=e12,[e13,e14]=ae13,[e13,e15]=e13,[e13,e17]=e13,[e19,e20]=2e20,[e19,e21]=e21,[e19,e22]=e22,[e19,e24]=e24,[e19,e25]=2e25,[e19,e26]=e26,[e20,e21]=e22,[e20,e23]=e20,[e20,e25]=2e19,[e20,e26]=2e24,[e21,e23]=e21,[e21,e24]=e19+e23,[e21,e26]=e25,[e22,e23]=2e22,[e22,e24]=e20,[e22,e25]=2e21,[e22,e26]=2e23,[e23,e24]=e24,[e23,e25]=e25,[e23,e26]=2e26,[e24,e25]=e26. (5.16)

    We describe the symmetry algebra by the following proposition:

    Proposition 4. The symmetry Lie algebra is a twenty-six-dimensional Lie algebra. It is a semi-direct product of an eighteen-dimensional solvable Lie algebra and sl(3, R ). The solvable part is ( R 13 R 5), a semi-direct product of  R 13 and  R 5. Therefore, the symmetry algebra can be identified as ( R 13 R 5)sl(3, R ).

    The geodesic equations are given by

    ¨p=˙p(a˙z+˙w),¨q=˙q˙z,¨x=˙y˙w,¨y=˙z˙w,¨z=0,¨w=0. (5.17)

    For the general case Aa0,ϵ=16,22, the symmetry Lie algebra is spanned by

    e1=Dt,e2=tDx,e3=Dp,e4=Dx,e5=Dy,e6=Dq,e7=Dw,e8=Dz,e9=tDt,e10=pDp,e11=wDt,e12=zDt,e13=zDx,e14=wDx,e15=qDq,e16=yDx+zDy,e17=ezDq,e18=twDx+2tDy,e19=w22Dx+wDy,e20=wzDx+2zDy,e21=(yz2y)Dt,e22=eweazDp,e23=(wyw2z2)Dx+(wz+2y)Dy. (5.18)

    We consider the following change of basis:

    ¯e1=e1,¯e2=e2,¯e3=e3,¯e4=e4,¯e5=e5,¯e6=e6,¯e7=e11,¯e8=e12,¯e9=e13,¯e10=e14,¯e11=e16,¯e12=e17,¯e13=e19,¯e14=e20,¯e15=e22,¯e16=e7,¯e17=e8,¯e18=e9+e232,¯e19=e10,¯e20=e15,¯e21=e9e232,¯e22=e18,¯e23=e21. (5.19)

    The nonzero brackets of the symmetry algebra are given by

    [e1,e2]=e4,[e1,e18]=e1,[e1,e21]=e1,[e1,e22]=e10+2e5,[e2,e7]=e10,[e2,e8]=e9,[e2,e18]=e2,[e2,e21]=e2,[e2,e23]=2e11e14,[e3,e19]=e3,[e5,e11]=e4,[e5,e18]=e5+e102,[e5,e21]=e5e102,[e5,e23]=2e1,[e6,e20]=e6,[e7,e16]=e1,[e7,e18]=e7,[e7,e21]=e7,[e7,e22]=2e13,[e8,e17]=e1,[e8,e18]=e8,[e8,e21]=e8,[e8,e22]=e14,[e9,e17]=e4,[e10,e16]=e4,[e11,e13]=e10,[e11,e14]=e9,[e11,e17]=e5,[e11,e18]=e11+e14,[e11,e21]=e11e14,[e11,e22]=2e2,[e11,e23]=2e8,[e12,e17]=e12,[e12,e20]=e12,[e13,e16]=e10e5,[e13,e18]=e18,[e13,e21]=e13,[e13,e23]=2e7,[e14,e16]=e9,[e14,e17]=e102e5,[e14,e18]=e14,[e14,e21]=e14,[e14,e23]=4e8,[e15,e16]=e15,[e15,e17]=ae15,[e15,e19]=e15,[e16,e18]=e112e142,[e16,e21]=e112+e142,[e16,e22]=e2,[e16,e23]=e8,[e17,e18]=e132,[e17,e21]=e132,[e17,e23]=e7,[e21,e22]=2e22,[e21,e23]=2e23,[e22,e23]=4e21. (5.20)

    We describe the symmetry algebra by the following proposition:

    Proposition 5. The symmetry Lie algebra is a twenty-three-dimensional Lie algebra. It is a semi-direct product of twenty-dimensional solvable Lie algebra and sl(2, R ). The solvable part is ( R 15 R 5), a semi-direct product of  R 15 and  R 5. Therefore, the symmetry algebra can be identified as ( R 15 R 5)sl(2, R ).

    The nonzero brackets for the algebra Aaϵ6,23 are given by

    [e1,e3]=e3,[e1,e4]=e4,[e1,e5]=e6,[e2,e3]=e4,[e2,e4]=e3,[e2,e5]=ae6,[e1,e2]=ϵe5. (5.21)

    The geodesic equations when ϵ=0 are given by

    ¨p=˙p˙z˙q˙w,¨q=˙p˙w+˙q˙z,¨x=˙y(˙z+a˙w),¨y=0,¨z=0,¨w=0. (5.22)

    The symmetry Lie algebra is spanned by

    e1=Dt,e2=Dp,e3=Dq,e4=tDx,e5=Dx,e6=Dy,e7=Dw,e8=Dz,e9=tDt,e10=wDt,e11=yDt,e12=zDt,e13=yDx,e14=zDx,e15=wDx,e16=pDp+qDq,e17=xDx+yDy,e18=qDppDq,e19=t(aw+z)2Dx+tDy,e20=((aw+z)y2x)Dx,e21=w(aw+z)2Dx+wDy,e22=z(aw+z)2Dx+zDy,e23=((aw+z)y2x)aDt,e24=cos(w)ezDp+sin(w)ezDq,e25=sin(w)ezDpcos(w)ezDq,e26=(aw2+z2)(awy+yz2x)aDx+((aw+z)y2x)aDy. (5.23)

    We consider the following change of basis:

    ¯e1=e1,¯e2=e2,¯e3=e3,¯e4=e5,¯e5=e6,¯e6=e10,¯e7=e12,¯e8=e14,¯e9=e15,¯e10=e21,¯e11=e22,¯e12=e24,¯e13=e25,¯e14=e7+ae132,¯e15=e8+e132,¯e16=e9+e17,¯e17=e16,¯e18=e18,¯e19=e4,¯e20=e9+e202,¯e21=e11,¯e22=e13,¯e23=e17+e20,¯e24=e19,¯e25=e23,¯e26=e26. (5.24)

    The nonzero brackets of the symmetry algebra are given by

    [e1,e16]=e1,[e1,e19]=e4,[e1,e20]=e1,[e1,e24]=ae92+e5+e82,[e2,e17]=e2,[e2,e18]=e3,[e3,e17]=e3,[e3,e18]=e2,[e4,e16]=e4,[e4,e20]=e4,[e4,e23]=e4,[e4,e25]=2e1a,[e4,e26]=e9e8a2e5a,[e5,e14]=ae42,[e5,e15]=e42,[e5,e16]=e5,[e5,e20]=ae92+e82,[e5,e21]=e1,[e5,e22]=e4,[e5,e23]=ae9+e5+e8,[e5,e25]=e7a+e6,[e5,e26]=e11a+e10,[e6,e14]=e1,[e6,e16]=e6,[e6,e19]=e9,[e6,e20]=e6,[e6,e24]=e10,[e7,e15]=e1,[e7,e16]=e7,[e7,e19]=e8,[e7,e20]=e7,[e7,e24]=e11,[e8,e15]=e4,[e8,e16]=e8,[e8,e20]=e8,[e8,e23]=e8,[e8,e25]=2e7a,[e8,e26]=2e11a,[e9,e14]=e4,[e9,e16]=e9,[e9,e20]=e9,[e9,e23]=e9,[e9,e25]=2e6a,[e9,e26]=2e10a,[e10,e14]=ae92e5e82,[e10,e16]=e10,[e10,e21]=e6,[e10,e22]=e9,[e10,e23]=e10,[e11,e15]=ae92e5e82,[e11,e16]=e11,[e11,e21]=e7,[e11,e22]=e8,[e11,e23]=e11.[e12,e14]=e13,[e12,e15]=e12,[e12,e17]=e12,[e12,e18]=e13,[e13,e14]=e12,[e13,e15]=e13,[e13,e17]=e13,[e13,e18]=e12,[e19,e20]=2e19,[e19,e21]=e22,[e19,e23]=e19,[e19,e25]=2e20a,[e19,e26]=2e24a,[e20,e21]=e21,[e20,e22]=e22,[e20,e24]=e24,[e20,e25]=2e25,[e20,e26]=e26,[e21,e23]=e21,[e21,e24]=e20+e23,[e21,e26]=e25,[e22,e23]=2e22,[e22,e24]=e19,[e22,e25]=2e21a,[e22,e26]=2e23a,[e23,e24]=e24,[e23,e25]=e25,[e23,e26]=2e26,[e24,e25]=e26. (5.25)

    We describe the symmetry algebra by the following proposition:

    Proposition 6. The symmetry Lie algebra is a twenty-six- dimensional Lie algebra. It is a semi-direct product of eighteen-dimensional solvable Lie algebra and sl(3, R ). The solvable part is ( R 13 R 5), a semi-direct product of  R 13 and  R 5. Therefore, the symmetry algebra can be identified as ( R 13 R 5)sl(3, R ).

    For Aa0,ϵ=16,23, the geodesic equations are given by

    ¨p=˙p˙z+˙w˙q,¨q=˙p˙w+˙q˙z,¨x=˙y(˙z+a˙w),¨y=˙z(˙z+a˙w),¨z=0,¨w=0. (5.26)

    The symmetry Lie algebra is spanned by

    e1=Dw,e2=Dq,e3=Dp,e4=Dy,e5=tDx,e6=Dz,e7=Dx,e8=tDt,e9=Dt,e10=wDx,e11=zDx,e12=wDt,e13=zDt,e14=yDx+zDy,e15=pDp+qDq,e16=qDp+pDq,e17 =t(aw+z)Dx2+tDy,e18=(awz+z22y)Dxa,e19=(awz+z22y)Dta,e20=(a2w22z22+y)Dxa+wDy,e21=sin(w)ezDp+cos(w)ezDq,e22=cos(w)ezDp+sin(w)ezDq,e23=(aw2+z2)(awz+z22y)Dxa+(awz+z22y)Dya. (5.27)

    We consider the following change of basis:

    ¯e1=e4,¯e2=e5,¯e3=e7,¯e4=e9,¯e5=e10,¯e6=e11,¯e7=e12,¯e8=e13,¯e9=e14,¯e10=e18,¯e11=e20,¯e12=e2,¯e13=e3,¯e14=e21,¯e15=e22,¯e16=e1,¯e17=e6,¯e18=e8ae232,¯e19=e15,¯e20=e16,¯e21=e8+ae232,¯e22=e17,¯e23=e19. (5.28)

    The nonzero brackets of the symmetry algebra are given by

    [e1,e9]=e3,[e1,e10]=2e3a,[e1,e11]=e3a,[e1,e18]=e62+ae52+e1,[e1,e21]=e62ae52e1,[e1,e23]=2e42,[e2,e4]=e3,[e2,e7]=e5,[e2,e8]=e6,[e2,e18]=e2,[e2,e21]=e2,[e2,e23]=e10,[e4,e18]=e4,[e4,e21]=e4,[e4,e22]=e62+ae52+e1,[e5,e16]=e3,[e6,e17]=e3,[e7,e16]=e4,[e7,e18]=e7,[e7,e21]=e7,[e7,e22]=e102+e11,[e8,e17]=e4,[e8,e18]=e8,[e8,e21]=e8,[e8,e22]=ae102+e9,[e9,e10]=2e6a,[e9,e11]=e6ae5,[e9,e17]=e1,[e9,e18]=ae10+e9,[e9,e21]=ae10e9,[e9,e22]=e2,[e9,e23]=2e8a,[e10,e11]=2e5a,[e10,e16]=e6,[e10,e17]=2e6ae5,[e10,e18]=e10,[e10,e21]=e10,[e10,e22]=2e2a,[e11,e16]=ae5e1,[e11,e17]=e6a,[e11,e18]=e10+e11,[e11,e21]=e10e11,[e11,e22]=e2a,[e11,e23]=2e7a,[e12,e19]=e12,[e12,e20]=e13,[e13,e19]=e13,[e13,e20]=e12,[e14,e16]=e15,[e14,e17]=e14,[e14,e19]=e14,[e14,e20]=e15. (5.29)
    [e15,e16]=e14,[e15,e17]=e15,[e15,e19]=e15,[e15,e20]=e14,[e16,e18]=a2e102ae92,[e16,e21]=a2e102+ae92,[e16,e22]=ae22,[e16,e23]=e8[e17,e18]=ae112ae10e9,[e17,e21]=ae112+ae10+e9,[e17,e22]=e22,[e17,e23]=2e8a+e7,[e21,e22]=2e22,[e21,e23]=2e23,[e22,e23]=2e21a. (5.30)

    We describe the symmetry algebra by the following proposition:

    Proposition 7. The symmetry Lie algebra is a twenty-three- dimensional semi-direct product of twenty- dimensional solvable Lie algebra S1,20 and sl(2, R ). The nilradical a fifteen-dimensional nilpotant Lie algebra N1,11 R 4, which is a direct sum of N1,11, an eleven-dimensional nilpotent Lie algebra, and a four-dimensional abelian Lie algebra  R 4. The complement to the nilradical is a four-dimensional non-abelian. Therefore, the symmetry Lie algebra can be identified as S1,20sl(2, R ).

    The nonzero brackets for the algebra A6,24 are given by

    [e1,e5]=e5+e6,[e1,e6]=e6,[e2,e4]=e4,[e1,e2]=e3. (5.31)

    The geodesic equations are given by

    ¨p=˙p˙z,¨q=˙w(˙q+˙x),¨x=˙x˙w,¨y=˙z˙w,¨z=0,¨w=0. (5.32)

    The symmetry Lie algebra is spanned by

    e1=Dt,e2=tDy,e3=Dy,e4=Dp,e5=Dq,e6=Dx,e7=Dz,e8=Dw,e9=tDt,e10=pDp,e11=wDt,e12=zDt,e13=wDy,e14=zDy,e15=xDq,e16=qDq+xDx,e17=ewDq,e18=ezDp,e19=(wz2y)Dt,e20=(wz2y)Dy,e21=(w1)ewDq+ewDx. (5.33)

    We consider the following change of basis:

    ¯e1=e5,¯e2=e17,¯e3=e6,¯e4=e21,¯e5=e15,¯e6=e1,¯e7=e3,¯e8=e4,¯e9=e11,¯e10=e12,¯e11=e13,¯e12=e14,¯e13=e18,¯e14=e7+e132,¯e15=e8+e142,¯e16=e9e202,¯e17=e10,¯e18=e16,¯e19=e2,¯e20=e9+e202,¯e21=e19, (5.34)

    and the nonzero brackets of the symmetry algebra are given by

    [e1,e18]=e1,[e2,e15]=e2,[e2,e18]=e2,[e3,e5]=e1,[e3,e18]=e3,[e4,e5]=e2,[e4,e15]=e2e4,[e4,e18]=e4,[e6,e16]=e6,[e6,e19]=e7,[e6,e20]=e6,[e7,e16]=e7,[e7,e20]=e7,[e7,e21]=2e6,[e8,e17]=e8,[e9,e15]=e6,[e9,e16]=e9,[e9,e19]=e11,[e9,e20]=e9,[e10,e14]=e6,[e10,e16]=e10,[e10,e19]=e12,[e10,e20]=e10,[e11,e15]=e7,[e11,e16]=e11,[e11,e20]=e11,[e11,e21]=2e9,[e12,e14]=e7,[e12,e16]=e12,[e12,e20]=e12,[e12,e21]=2e10,[e13,e14]=e13,[e13,e17]=e13,[e19,e20]=2e19,[e19,e21]=2e20,[e20,e21]=2e21. (5.35)

    We describe the symmetry algebra by the following proposition:

    Proposition 8. The symmetry Lie algebra is a twenty-one-dimensional Lie algebra. It is a semi-direct product of an eighteen-dimensional solvable Lie algebra and sl(2, R ). The nilradical is a thirteen-dimensional decomposable Lie algebra. In fact, the nilradical is a direct sum of A5,1 in Winternitz [5] and  R 8. The nilradical has a five-dimensional abelian complement. Therefore, the symmetry algebra can be identified as ((A5,1 R 8) R 5)sl(2, R ), where the nonzero brackets of A5.1 are given by

    [e3,e5]=e1,[e4,e5]=e2. (5.36)

    The nonzero brackets for the algebra Aab6,25 are given by

    [e1,e4]=ae4,[e1,e5]=e6,[e1,e6]=e5,[e2,e4]=be4,[e2,e5]=e5,[e2,e6]=e6,[e1,e2]=e3. (5.37)

    The geodesic equations are given by

    ¨p=˙p(b˙w+a˙z),¨q=˙w˙z,¨x=˙x˙z˙y˙w,¨y=˙x˙w˙y˙z,¨z=0,¨w=0. (5.38)

    For the general case Aa0,b06,25, the symmetry Lie algebra is spanned by

    e1=Dt,e2=tDq,e3=Dq,e4=Dy,e5=Dx,e6=Dp,e7=Dw,e8=Dz,e9=tDt,e10=pDp,e11=wDq,e12=zDq,e13=wDt,e14 =zDt,e15=xDx+yDy,e16=(wz+2q)Dq,e17=(wz+2q)Dt,e18=ebweazDp. (5.39)

    We implement the following change of basis:

    ¯e1=e1,¯e2=e3,¯e3=e13,¯e4=e11,¯e5=e7+be8a,¯e6=e4,¯e7=e5,¯e8=e6,¯e9=e11+ae12b,¯e10=e13+ae14b,¯e11=e18,¯e12=e8e112,¯e13=e9+e162,¯e14=e10,¯e15=e15,¯e16=e2,¯e17=e9e162,¯e18=e17, (5.40)

    and the nonzero brackets of the symmetry algebra are given by

    [e1,e13]=e1,[e1,e16]=e2,[e1,e17]=e1,[e2,e13]=e2,[e2,e17]=e2,[e2,e18]=2e1,[e3,e5]=e1,[e3,e13]=e3,[e3,e16]=e4,[e3,e17]=e3,[e4,e5]=e2,[e4,e13]=e4,[e4,e17]=e4,[e4,e18]=2e3,[e5,e12]=e22,[e5,e13]=be92a+be4a,[e5,e17]=be92abe4a,[e5,e18]=be10a+2be3a,[e6,e15]=e6,[e7,e15]=e7,[e8,e14]=e8,[e9,e12]=ae2b,[e9,e13]=e9,[e9,e17]=e9,[e9,e18]=2e10,[e10,e12]=ae1b,[e10,e13]=e10,[e10,e16]=e9,[e10,e17]=e10,[e11,e12]=ae11,[e11,e14]=e11,[e16,e17]=2e16,[e16,e18]=2e17,[e17,e18]=2e18. (5.41)

    We describe the symmetry algebra by the following proposition:.

    Proposition 9. The symmetry Lie algebra is an eighteen-dimensional Lie algebra. It is a semi-direct product of fifteen-dimensional solvable Lie algebra and sl(2, R ). The nilradical is an eleven-dimensional decomposable Lie algebra. In fact, the nilradical is a direct sum of A5,1 in Winternitz [5] and  R 6. The nilradical has a four-dimensional abelian complement. Therefore, the symmetry algebra can be identified as ((A5,1 R 4) R 5)sl(2, R ), where the nonzero brackets of A5.1 are given by

    [e3,e5]=e1,[e4,e5]=e2. (5.42)

    The nonzero brackets for the algebra Aa6,26 are given by

    [e1,e5]=ae5+e6,[e1,e6]=ae6e5,[e2,e4]=e4,[e1,e2]=e3. (5.43)

    The geodesic equations are given by

    ¨p=˙p˙z,¨q=˙w˙z,¨x=˙w(a˙x˙y),¨y=˙w(˙x+a˙y),¨z=0,¨w=0. (5.44)

    For the general case Aa06,26, the symmetry Lie algebra is spanned by

    e1=Dt,e2=tDq,e3=Dq,e4=Dx,e5=Dp,e6=Dy,e7=Dz,e8=Dw,e9=tDt,e10=pDp,e11=wDq,e12=zDq,e13=wDt,e14=zDt,e15=xDx+yDy,e16=ezDp,e17=yDxxDy,e18=(wz+2q)Dq,e19=(wz+2q)Dt,e20=eawcos(w)Dx+eawsin(w)Dy,e21=eawsin(w)Dxeawcos(w)Dy. (5.45)

    We consider the following change of basis:

    ¯e1=e1,¯e2=e3,¯e3=e4,¯e4=e5,¯e5=e6,¯e6=e11,¯e7=e12,¯e8=e13,¯e9=e14,¯e10=e16,¯e11=e20,¯e12=e21,¯e13=e7+e112,¯e14=e8+e122,¯e15=e9e182,¯e16=e10,¯e17=e15,¯e18=e17,¯e19=e2,¯e20=e9+e182,¯e21=e19. (5.46)

    The nonzero brackets of the symmetry algebra are given by

    [e1,e15]=e1,[e1,e19]=e2,[e1,e20]=e1,[e2,e15]=e2,[e2,e20]=e2,[e2,e21]=2e1,[e3,e17]=e3,[e3,e18]=e5,[e4,e16]=e4,[e5,e17]=e5,[e5,e18]=e3,[e6,e14]=e2,[e6,e15]=e6,[e6,e20]=e6,[e6,e21]=2e8,[e7,e13]=e2,[e7,e15]=e7,[e7,e20]=e7,[e7,e21]=2e9,[e8,e14]=e1,[e8,e15]=e8,[e8,e19]=e6,[e8,e20]=e8,[e9,e13]=e1,[e9,e15]=e9,[e9,e19]=e7,[e9,e20]=e9,[e10,e13]=e10,[e10,e16]=e10,[e11,e14]=ae11+e12,[e11,e17]=e11,[e11,e18]=e12,[e12,e14]=ae12e11,[e12,e17]=e12,[e12,e18]=e11,[e19,e20]=2e19,[e19,e21]=2e20,[e20,e21]=2e21. (5.47)

    We describe the symmetry algebra by the following proposition:

    Proposition 10. The symmetry Lie algebra is a twenty-one-dimensional Lie algebra. It is a semi-direct product of an eighteen-dimensional solvable Lie algebra and sl(2, R ). The nilradical is twelve-dimensional abelian Lie algebra and has a six-dimensional abelian complement. Therefore, the symmetry algebra can be identified as: ( R 12 R 6)sl(2, R ).

    The symmetry Lie algebra is spanned by

    e1=Dt,e2=tDq,e3=Dq,e4=Dp,e5=Dx,e6=Dy,e7=Dz,e8=Dw,e9=tDt,e10=pDp,e11=wDq,e12=zDp,e13=wDt,e14=zDt,e15=xDx+yDy,e16=ezDp,e17=yDxxDy,e18=cos(w)Dx+sin(w)Dy,e19=(wz2q)Dq,e20=(wz2q)Dt,e21=sin(w)Dxcos(w)Dy,e22=(cos(w)y+xsin(w))Dx+(cos(w)xysin(w))Dy,e23=(cos(w)x+ysin(w))Dx+(cos(w)y+xsin(w))Dy. (5.48)

    We implement the following change of basis

    ¯e1=e1,¯e2=e3,¯e3=e4,¯e4=e5,¯e5=e6,¯e6=e11,¯e7=e12,¯e8=e13,¯e9=e14,¯e10=e16,¯e11=e18,¯e12=e21,¯e13=e7+e112,¯e14=e8e172+e122,¯e15=e9e192,¯e16=e10,¯e17=e15,¯e18=e2,¯e19=e9+e192,¯e20=e20,¯e21=e17,¯e22=e22,¯e23=e23, (5.49)

    and the nonzero brackets of the symmetry algebra are given by

    [e1,e15]=e1,[e1,e18]=e2,[e1,e19]=e1,[e2,e15]=e2,[e2,e19]=e2,[e2,e20]=2e1,[e3,e16]=e3,[e4,e14]=e52,[e4,e17]=e4,[e4,e21]=e5,[e4,e22]=e12,[e4,e23]=e11,[e5,e14]=e42,[e5,e17]=e5,[e5,e21]=e4,[e5,e22]=e11,[e5,e23]=e12,[e6,e14]=e2,[e6,e15]=e6,[e6,e19]=e6,[e6,e20]=2e8,[e7,e13]=e2,[e7,e15]=e7,[e7,e19]=e7,[e7,e20]=2e9,[e8,e14]=e1,[e8,e15]=e8,[e8,e18]=e6,[e8,e19]=e8,[e9,e13]=e1,[e9,e15]=e9,[e9,e18]=e7,[e9,e19]=e9,[e10,e13]=e10,[e10,e16]=e10,[e11,e14]=e122,[e11,e17]=e11,[e11,e21]=e12,[e11,e22]=e5,[e11,e23]=e4,[e12,e14]=e112,[e12,e17]=e12,[e12,e21]=e11,[e12,e22]=e4,[e12,e23]=e5,[e18,e19]=e18,[e18,e20]=2e19,[e19,e20]=2e20,[e21,e22]=2e23,[e21,e23]=2e22,[e22,e23]=2e21. (5.50)

    We describe the symmetry algebra by the following proposition:

    Proposition 11. The symmetry Lie algebra is a twenty-three-dimensional Lie algebra. It is a semi-direct product of a seventeen-dimensional solvable Lie algebra and two copies of sl(2, R ). Furthermore, the symmetry Lie algebra has a twelve-dimensional abelian nilradical and five-dimensional abelian complement. Therefore, the symmetry algebra can be identified as

    (R12R5)(sl(2,R)sl(2,R)).

    The nonzero brackets for the algebra Aϵ6,27 are given by

    [e1,e3]=e4,[e1,e5]=e6,[e1,e6]=e5,[e2,e5]=e5,[e2,e6]=e6,[e1,e2]=ϵe3. (5.51)

    The geodesic equations where ϵ=0 are given by

    ¨p=˙p˙w˙q˙z,¨q=˙p˙z+˙q˙w,¨x=0,¨y=˙x˙z,¨z=0,¨w=0. (5.52)

    The symmetry Lie algebra is spanned by

    e1=Dt,e2=tDy,e3=Dy,e4=Dp,e5=Dq,e6=Dx,e7=Dw,e8=Dz,e9=tDt,e10=wDt,e11=xDt,e12=zDt,e13=wDy,e14=xDy,e15=zDy,e16=pDp+qDq,e17=xDx+yDy,e18=qDppDq,e19=tDx+tz2Dy,e20=zDx+z22Dy,e21=(xz2y)Dt,e22 =(xz2y)Dy,e23=wDx+wz2Dy,e24=ewcos(z)Dp+ewsin(z)Dq,e25=ewsin(z)Dpewcos(z)Dq,e26=(xz2y)Dx+(xz22yz)Dy. (5.53)

    We implement the following change of basis:

    ¯e1=e1,¯e2=e3,¯e3=e4,¯e4=e5,¯e5=e6,¯e6=e10,¯e7=e12,¯e8=e13,¯e9=e15,¯e10=e20,¯e11=e23,¯e12=e24,¯e13=e25,¯e14=e7,¯e15=e8+e142,¯e16=e9+e17,¯e17=e16,¯e18=e18,¯e19=e2,¯e20=e9+e222,¯e21=e11,¯e22=e14,¯e23=e17+e22,¯e24=e19,¯e25=e21,¯e26=e26, (5.54)

    and the nonzero brackets of the symmetry algebra are given by

    [e1,e16]=e1,[e1,e19]=e2,[e1,e20]=e1,[e1,e24]=e5+e92,[e2,e16]=e2,[e2,e20]=e2,[e2,e23]=e2,[e2,e25]=2e1,[e2,e26]=2e5e9,[e3,e17]=e3,[e3,e18]=e4,[e4,e17]=e4,[e4,e18]=e3,[e5,e15]=e22,[e5,e16]=e5,[e5,e20]=e92,[e5,e21]=e1,[e5,e22]=e2,[e5,e23]=e5+e9,[e5,e25]=e7,[e5,e26]=e10,[e6,e14]=e1,[e6,e16]=e6,[e6,e19]=e8,[e6,e20]=e6,[e6,e24]=e11,[e7,e15]=e1,[e7,e16]=e7,[e7,e19]=e9,[e7,e20]=e7,[e7,e24]=e10,[e8,e14]=e2,[e8,e16]=e8,[e8,e20]=e8,[e8,e23]=e8,[e8,e25]=2e6,[e8,e26]=2e11,[e9,e15]=e2,[e9,e16]=e9,[e9,e20]=e9,[e9,e23]=e9,[e9,e25]=2e7,[e9,e26]=2e10,[e10,e15]=e5e92,[e10,e16]=e10,[e10,e21]=e7,[e10,e22]=e9,[e10,e23]=e10,[e11,e14]=e5e92,[e11,e16]=e11,[e11,e21]=e6,[e11,e22]=e8,[e11,e23]=e11,[e12,e14]=e12,[e12,e15]=e13,[e12,e17]=e12,[e12,e18]=e13,[e13,e14]=e13,[e13,e15]=e12,[e13,e17]=e13,[e13,e18]=e12,[e19,e20]=2e19,[e19,e21]=e22,[e19,e23]=e19,[e19,e25]=2e20,[e19,e26]=2e24,[e20,e21]=e21,[e20,e22]=e22,[e20,e24]=e24,[e20,e25]=2e25,[e20,e26]=e26,[e21,e23]=e21,[e21,e24]=e20+e23,[e21,e26]=e25,[e22,e23]=2e22,[e22,e24]=e19,[e22,e25]=2e21,[e22,e26]=2e23,[e23,e24]=e24,[e23,e25]=e25,[e23,e26]=2e26,[e24,e25]=e26. (5.55)

    We describe the symmetry algebra by the following proposition:

    Proposition 12. The symmetry Lie algebra is a twenty-six-dimensional semi-direct product of an eighteen solvable Lie algebra and eight-dimensional semi-simple sl(3, R ). Furthermore, the symmetry Lie algebra has a thirteen-dimensional abelian nilradical. Therefore, the symmetry algebra can be identified as: ( R 13 R 5)sl(3, R ).

    The geodesic equations where ϵ=1 are given by

    ¨p=˙q˙w,¨q=˙z˙w,¨x=˙z˙x˙w˙y,¨y=˙z˙y+˙w˙x,¨z=0,¨w=0. (5.56)

    The symmetry Lie algebra is spanned by

    e1=Dz,e2=Dp,e3=Dx,e4=Dw,e5=Dy,e6=Dq,e7=tDt,e8=Dt,e9=tDp,e10=zDp,e11=wDp,e12=wDt,e13=zDt,e14=qDp+zDq,e15=xDx+yDy,e16=yDxxDy,e17=twDp+2tDq,e18=w22Dp+wDq,e19=wzDp+2zDq,e20=(wz2q)Dt,e21=ezcos(w)Dx+ezsin(w)Dy,e22=ezsin(w)Dxezcos(w)Dy,e23=(qwzw22)Dp+(wz+2q)Dq. (5.57)

    We implement the following change of basis:

    ¯e1=e2,¯e2=e6,¯e3=e8,¯e4=e9,¯e5=e10,¯e6=e11,¯e7=e12,¯e8=e13,¯e9=e14,¯e10=e18,¯e11=e19,¯e12=e3,¯e13=e5,¯e14=e21,¯e15=e22,¯e16=e1,¯e17=e4,¯e18=e7+e232,¯e19=e15,¯e20=e16,¯e21=e7e232,¯e22=e17,¯e23=e20, (5.58)

    and the nonzero brackets of the symmetry algebra are given by

    [e2,e9]=e1,[e2,e18]=e2+e62,[e2,e21]=e2e62,[e2,e23]=2e3,[e3,e4]=e1,[e3,e18]=e3,[e3,e21]=e3,[e3,e22]=2e2+e6,[e4,e7]=e6,[e4,e8]=e5,[e4,e18]=e4,[e4,e21]=e4,[e4,e23]=e11+2e9,[e5,e16]=e1,[e6,e17]=e1,[e7,e17]=e3,[e7,e18]=e7,[e7,e21]=e7,[e7,e22]=2e10,[e8,e16]=e3,[e8,e18]=e8,[e8,e21]=e8,[e8,e22]=e11,[e9,e10]=e6,[e9,e11]=2e5,[e9,e16]=e2,[e9,e18]=e11e9,[e9,e21]=e11+e9,[e9,e22]=2e4,[e9,e23]=2e8,[e10,e17]=e2e6,[e10,e18]=e10,[e10,e21]=e10,[e10,e23]=2e7,[e11,e16]=2e2e6,[e11,e17]=e5,[e11,e18]=e11,[e11,e21]=e11,[e11,e23]=4e8,[e12,e19]=e12,[e12,e20]=e13,[e13,e19]=e13,[e13,e20]=e12,[e14,e16]=e14,[e14,e17]=e15,[e14,e19]=e14,[e14,e20]=e15,[e15,e16]=e15,[e15,e17]=e14,[e15,e19]=e15,[e15,e20]=e14,[e16,e18]=e102,[e16,e21]=e102,[e16,e23]=e7,[e17,e18]=e112+e92,[e17,e21]=e112e92,[e17,e22]=e4,[e17,e23]=e8,[e21,e22]=2e22,[e21,e23]=2e23,[e22,e23]=4e21. (5.59)

    We describe the symmetry algebra by the following proposition:

    Proposition 13. The symmetry Lie algebra is a twenty-three-dimensional semi-direct product of twenty-dimensional solvable Lie algebra S2,20, and sl(2, R ). The nilradical is a fifteen-dimensional nilpotant Lie algebra N2,11 R 4, which is a direct sum of N2,11, an eleven-dimensional nilpotent Lie algebra, and a four-dimensional abelian Lie algebra  R 4. The complement of the nilradical is four-dimensional non-abelian. Therefore, the symmetry Lie algebra can be identified as S2,20sl(2, R ).

    In this work, we have investigated the symmetry Lie algebra of the geodesic equations of the canonical connection on a Lie group corresponding to the eight classes of Lie algebra A6,20A6,27 in [7]. In each case, we list the nonzero brackets of the given Lie algebra, the geodesic equations, and a basis for the symmetry Lie algebra in terms of vector fields. For every symmetry Lie algebra, we identify its nilradical, solvable complement, and semi-simple factor; a summary of our results is given in Table 1. In future work, we plan to study the symmetry Lie algebras for the rest of the six-dimensional Lie algebras A6,28A6,40 in [7]. The results help to put symmetry Lie algebras into context since they are of very high dimension. It remains to use the symmetries to help integrate the geodesic equations. Another useful by-product is the construction of many large dimensional Levi decomposition Lie algebras, which is a topic of independent interest.

    Table 1.  Six-dimensional Lie algebras and identification of the symmetry algebra.
    Six-dimensional Lie algebras Dimension Identification
    Aab6,20 (ab:a2+b20) 21 ( R 12 R 6)sl(2, R )
    Aa6,21 21 ((A5,1 R 8) R 5)sl(2, R )
    Aϵ=06,22 26 ( R 13 R 5)sl(3, R )
    Aϵ=16,22 23 (R15 R 5)sl(2, R )
    Aa,ϵ=06,23 26 ( R 13 R 5)sl(3, R )
    Aa,ϵ=16,23 23 S1,20sl(2, R )
    A6,24 21 ((A5.1 R 8) R 5)sl(2, R )
    Aab6,25 (ab:a2+b20) 18 ((A5,1 R 4) R 5)sl(2, R )
    Aa6,26 21 ( R 12 R 6)sl(2, R )
    Aa=06,26 23 ( R 12 R 5)(sl(2, R )sl(2, R ))
    Aϵ=06,27 26 ( R 13 R 5)sl(3, R )
    Aϵ=16,27 23 S2,20sl(2, R )

     | Show Table
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    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    Nouf Almutiben would like to thank Jouf University and Virginia Commonwealth University for their support. Ryad Ghanam and Edward Boone would like to thank Qatar Foundation and Virginia Commonwealth University in Qatar for their support through the Mathematical Data Science Lab.

    The authors declare that they have no conflicts of interest.



    [1] V. Sharma, M. G. Dastidar, S. Sutradhar, V. Raj, K. De Silva, S. Roy, A step toward better sample management of COVID-19: On-spot detection by biometric technology and artificial intelligence, COVID-19 Sustain, Develop. Goals, 2022 (2022), 349–380. https://doi.org/10.1016/B978-0-323-91307-2.00017-1 doi: 10.1016/B978-0-323-91307-2.00017-1
    [2] G. Latif, H. Morsy, A. Hassan, J. Alghazo, Novel coronavirus and common pneumonia detection from CT scans using deep learning-based extracted features, Viruses, 14 (2022), 1667. https://doi.org/10.3390/v14081667 doi: 10.3390/v14081667
    [3] A. Islam, T. Rahim, M. Masuduzzaman, S. Y. Shin, A blockchain-based artificial intelligence-empowered contagious pandemic situation supervision scheme using internet of drone things, IEEE Wirel. Commun., 28 (2021), 166–173. https://doi.org/10.1109/MWC.001.2000429 doi: 10.1109/MWC.001.2000429
    [4] T. Rahim, M. A. Usman, S. Y. Shin, A survey on contemporary computer-aided tumor, polyp, and ulcer detection methods in wireless capsule endoscopy imaging, Comput. Med. Imag. Grap., 85 (2020), 101767. https://doi.org/10.1016/j.compmedimag.2020.101767 doi: 10.1016/j.compmedimag.2020.101767
    [5] G. Latif, DeepTumor: Framework for brain MR image classification, segmentation and tumor detection, Diagnostics, 12 (2022), 2888. https://doi.org/10.3390/diagnostics12112888 doi: 10.3390/diagnostics12112888
    [6] T. Rahim, S. A. Hassan, S. Y. Shin, A deep convolutional neural network for the detection of polyps in colonoscopy images, Biomed. Signal Proces., 68 (2021), 102654. https://doi.org/10.1016/j.bspc.2021.102654 doi: 10.1016/j.bspc.2021.102654
    [7] A. Bashar, G. Latif, G. Ben Brahim, N. Mohammad, J. Alghazo, COVID-19 pneumonia detection using optimized deep learning techniques, Diagnostics, 11 (2021), 1972. https://doi.org/10.3390/diagnostics11111972 doi: 10.3390/diagnostics11111972
    [8] E. Hussain, M. Hasan, M. A. Rahman, I. Lee, T. Tamanna, M. Z. Parvez, CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images, Chaos Soliton. Fract., 142 (2021), 110495. https://doi.org/10.1016/j.chaos.2020.110495 doi: 10.1016/j.chaos.2020.110495
    [9] G. Latif, G. Ben Brahim, D. N. F. A. Iskandar, A. Bashar, J. Alghazo, Glioma tumors' classification using deep-neural-network-based features with SVM classifier, Diagnostics, 12 (2022), 1018. https://doi.org/10.3390/diagnostics12041018 doi: 10.3390/diagnostics12041018
    [10] I. Iqbal, M. Younus, K. Walayat, M. U. Kakar, J. Ma, Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images, Comput. Med. Imag. grap, 88 (2021), 101843. https://doi.org/10.1016/j.compmedimag.2020.101843 doi: 10.1016/j.compmedimag.2020.101843
    [11] I. Iqbal, K. Walayat, M. U. Kakar, J. Ma, Automated identification of human gastrointestinal tract abnormalities based on deep convolutional neural network with endoscopic images, Intell. Syst. Appl., 16 (2022), 200149. https://doi.org/10.1016/j.iswa.2022.200149 doi: 10.1016/j.iswa.2022.200149
    [12] V. Shah, R. Keniya, A. Shridharani, M. Punjabi, J. Shah, N. Mehendale, Diagnosis of COVID-19 using CT scan images and deep learning techniques, Emerg. Radiol., 28 (2021), 497–505. https://doi.org/10.1007/s10140-020-01886-y doi: 10.1007/s10140-020-01886-y
    [13] M. M. Rahaman, C. Li, Y. Yao, K. Frank, M. A. Rahman, Q. Wang, et al., Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches, J. X-Ray Sci. Technol., 28 (2020), 821–839. https://doi.org/10.3233/XST-200715 doi: 10.3233/XST-200715
    [14] A. S. Al-Waisy, S. Al-Fahdawi, M. A. Mohammed, K. H. Abdulkareem, S. A. Mostafa, M. S. Maashi, et al., COVID-CheXNet: Hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images, Soft Comput., 27 (2020), 2657–2672. https://doi.org/10.1007/s00500-020-05424-3 doi: 10.1007/s00500-020-05424-3
    [15] Y. Chang, X. Jing, Z. Ren, B. Schuller, CovNet: A transfer learning framework for automatic COVID-19 detection from crowd-sourced cough sounds, Front. Digit. Health, 3 (2022), 799067. https://doi.org/10.3389/fdgth.2021.799067 doi: 10.3389/fdgth.2021.799067
    [16] M. Elpeltagy, H. Sallam, Automatic prediction of COVID-19 from chest images using modified ResNet50, Multimed. Tools Appl., 80 (2021), 26451–26463. https://doi.org/10.1007/s11042-021-10783-6 doi: 10.1007/s11042-021-10783-6
    [17] R. K. Patel, M. Kashyap, Automated diagnosis of COVID stages from lung CT images using statistical features in 2-dimensional flexible analytic wavelet transform, Biocybern. Biomed. Eng., 42 (2022), 829–841. https://doi.org/10.1016/j.bbe.2022.06.005 doi: 10.1016/j.bbe.2022.06.005
    [18] D. K. Redie, A. E. Sirko, T. M. Demissie, S. S. Teferi, V. K. Shrivastava, O. P. Verma, et al., Diagnosis of COVID-19 using chest X-ray images based on modified DarkCovidNet model, Evol Intell., 16 (2022), 729–738. https://doi.org/10.1007/s12065-021-00679-7 doi: 10.1007/s12065-021-00679-7
    [19] F. Özyurt, Efficient deep feature selection for remote sensing image recognition with fused deep learning architectures, J. Supercomput, 76 (2020), 8413–8431. https://doi.org/10.1007/s11227-019-03106-y doi: 10.1007/s11227-019-03106-y
    [20] D. H. Hubel, T. N. Wiesel, Receptive fields, binocular interaction and functional architecture in the cat's visual cortex, J. Physiol., 160 (1962), 106–154. https://doi.org/10.1113/jphysiol.1962.sp006837 doi: 10.1113/jphysiol.1962.sp006837
    [21] Y. LeCun, Y. Bengio, Convolutional networks for images, speech, and time series, In: The handbook of brain theory and neural networks, 1995.
    [22] G. Latif, J. Alghazo, L. Alzubaidi, M. N. Nasser, Y. Alghazo, Deep convolutional neural network for recognition of unified multi-language handwritten numerals, In: 2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR), 2018. https://doi.org/10.1109/ASAR.2018.8480289
    [23] A. Krizhevsky, I. Sutskever, G. E. Hinton, ImageNet classification with deep convolutional neural networks. Commun. ACM, 60 (2017), 84–90. https://doi.org/10.1145/3065386 doi: 10.1145/3065386
    [24] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, et al., Going deeper with convolutions, 2014, arXiv: 1409.4842.
    [25] S. Alghamdi, M. Alabkari, F. Aljishi, G. Latif, A. Bashar, Lung cancer detection from LDCT images using deep convolutional neural networks, In: International Conference on Communication, Computing and Electronics Systems, Singapore: Springer, 733 (2021), 363–374. https://doi.org/10.1007/978-981-33-4909-4_27
    [26] D. A. Alghmgham, G. Latif, J. Alghazo, L. Alzubaidi, Autonomous traffic sign (ATSR) detection and recognition using deep CNN, Procedia Comput. Sci., 163 (2019), 266–274. https://doi.org/10.1016/j.procs.2019.12.108 doi: 10.1016/j.procs.2019.12.108
    [27] G. Latif, N. Mohammad, R. AlKhalaf, R. AlKhalaf, J. Alghazo, M. Khan, An automatic arabic sign language recognition system based on deep CNN: An assistive system for the deaf and hard of hearing, Int. J. Comput. Digital Syst., 9 (2020), 715–724. http://doi.org/10.12785/ijcds/090418 doi: 10.12785/ijcds/090418
    [28] B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, A. Oliva, Learning deep features for scene recognition using places database, In: NIPS'14: Proceedings of the 27th International Conference on Neural Information Processing Systems, 1 (2014), 487–495.
    [29] M. M. Butt, G. Latif, D. N. F. A. Iskandar, J. Alghazo, A. H. Khan, Multi-channel convolutions neural network based diabetic retinopathy detection from fundus images, Procedia Comput. Sci., 163 (2019), 283–291. https://doi.org/10.1016/j.procs.2019.12.110 doi: 10.1016/j.procs.2019.12.110
    [30] D. C. Cireşan, U. Meier, J. Masci, L. Gambardella, J. Schmidhuber, Flexible, high performance convolutional neural networks for image classification, In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, 2011, 1237–1242. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-210
    [31] G. Lokku, G. H. Reddy, M. N. G. Prasad, OPFaceNet: Optimized face recognition network for noise and occlusion affected face images using hyperparameters tuned convolutional neural network, Appl. Soft Comput., 117 (2022), 108365. https://doi.org/10.1016/j.asoc.2021.108365 doi: 10.1016/j.asoc.2021.108365
    [32] S. Y. Kim, Z. W. Geem, G. Han, Hyperparameter optimization method based on harmony search algorithm to improve performance of 1D CNN human respiration pattern recognition system, Sensors, 20 (2020), 3697. https://doi.org/10.3390/s20133697 doi: 10.3390/s20133697
    [33] G. Latif, K. Bouchard, J. Maitre, A. Back, L. P. Bedard, Deep-learning-based automatic mineral grain segmentation and recognition, Minerals, 12 (2022), 455. https://doi.org/10.3390/min12040455 doi: 10.3390/min12040455
    [34] J. Bruna, S. Mallat, Invariant scattering convolution networks, IEEE T. Pattern Anal., 35 (2013), 1872–1886. https://doi.org/10.1109/TPAMI.2012.230 doi: 10.1109/TPAMI.2012.230
    [35] S. Lawrence, C. L. Giles, A. C. Tsoi, What size neural network gives optimal generalization? Convergence properties of backpropagation, In: Digital Repository at the University of Maryland, 1998.
    [36] L. Wan, M. Zeiler, S. Zhang, Y. Cun, R. Fergus, Regularization of neural networks using dropconnect, In: ICML'13: Proceedings of the 30th International Conference on International Conference on Machine Learning, 28 (2013), 1058–1066.
    [37] Q. Xu, M. Zhang, Z. Gu, G. Pan, Overfitting remedy by sparsifying regularization on fully-connected layers of CNNs, Neurocomputing, 328 (2019), 69–74. https://doi.org/10.1016/j.neucom.2018.03.080 doi: 10.1016/j.neucom.2018.03.080
    [38] S. R. Dubey, S. K. Singh, B. B. Chaudhuri, Activation functions in deep learning: A comprehensive survey and benchmark, Neurocomputing, 503 (2022), 92–108. https://doi.org/10.1016/j.neucom.2022.06.111 doi: 10.1016/j.neucom.2022.06.111
    [39] S. Akbar, M. Peikari, S. Salama, S. Nofech-Mozes, A. Martel, The transition module: A method for preventing overfitting in convolutional neural networks, Comput. Methods Biomech. Biomed. Eng.: Imaging Vis., 7 (2019), 260–265. https://doi.org/10.1080/21681163.2018.1427148 doi: 10.1080/21681163.2018.1427148
    [40] H. Wu, X. Gu, Towards dropout training for convolutional neural networks, Neural Networks, 71 (2015), 1–10. https://doi.org/10.1016/j.neunet.2015.07.007 doi: 10.1016/j.neunet.2015.07.007
    [41] M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, S. Mougiakakou, Lung pattern classification for interstitial lung diseases using a deep convolutional neural network, IEEE T. Med. Imaging, 35 (2016), 1207–1216. https://doi.org/10.1109/TMI.2016.2535865 doi: 10.1109/TMI.2016.2535865
    [42] J. Chen, Y. Shen, The effect of kernel size of CNNs for lung nodule classification, In: 2017 9th International Conference on Advanced Infocomm Technology (ICAIT), 2017,340–344. https://doi.org/10.1109/ICAIT.2017.8388942
    [43] B. Chen, W. Deng, J. Du, Noisy softmax: Improving the generalization ability of DCNN via postponing the early softmax saturation, In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, 4021–4030. https://doi.org/10.1109/CVPR.2017.428
    [44] Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle, Greedy layer-wise training of deep networks, In: Advances in Neural Information Processing Systems 19 (NIPS 2006), 2006.
    [45] K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016,770–778. https://doi.org/10.1109/CVPR.2016.90
    [46] S. Han, J. Pool, J. Tran, W. J. Dally, Learning both weights and connections for efficient neural network, In: NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems, 1 (2015), 1135–1143.
    [47] P. Ochs, A. Dosovitskiy, T. Brox, T. Pock, On iteratively reweighted algorithms for nonsmooth nonconvex optimization in computer vision, SIAM J. Imaging Sci., 8 (2015), 331–372. https://doi.org/10.1137/140971518 doi: 10.1137/140971518
    [48] P. Murugan, S. Durairaj, Regularization and optimization strategies in deep convolutional neural network, 2017, arXiv: 1712.04711.
    [49] J. Snoek, O. Rippel, K. Swersky, R. Kiros, N. Satish, N. Sundaram, et al., Scalable Bayesian optimization using deep neural networks, In: ICML'15: Proceedings of the 32nd International Conference on International Conference on Machine Learning, 37 (2015), 2171–2180.
    [50] D. Cheng, Y. Gong, S. Zhou, J. Wang, N. Zheng, Person re-identification by multi-channel parts-based CNN with improved triplet loss function, In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, 1335–1344. https://doi.org/10.1109/CVPR.2016.149
    [51] Y. S. Aurelio, G. M. de Almeida, C. L. de Castro, A. P. Braga, Learning from imbalanced data sets with weighted cross-entropy function, Neural Process Lett., 50 (2019), 1937–1949. https://doi.org/10.1007/s11063-018-09977-1 doi: 10.1007/s11063-018-09977-1
    [52] M. Bouten, J. Schietse, C. Van. den Broeck, Gradient descent learning in perceptrons: A review of its possibilities, Phys. Rev. E, 52 (1995), 1958–1967. https://doi.org/10.1103/PhysRevE.52.1958 doi: 10.1103/PhysRevE.52.1958
    [53] A. El-Sawy, M. Loey, H. El-Bakry, Arabic handwritten characters recognition using convolutional neural network, WSEAS Trans. Comput. Res., 5 (2017), 11–19.
    [54] Y. Sun, W. Zhang, H. Gu, C. Liu, S. Hong, W. Xu, et al., Convolutional neural network based models for improving super-resolution imaging, IEEE Access, 7 (2019), 43042–43051. https://doi.org/10.1109/ACCESS.2019.2908501 doi: 10.1109/ACCESS.2019.2908501
    [55] G. D. Rubin, C. J. Ryerson, L. B. Haramati, N. Sverzellati, J. P. Kanne, S. Raoof, et al., The role of chest imaging in patient management during the COVID-19 pandemic: A multinational consensus statement from the Fleischner society, Radiology, 296 (2020), 172–180. https://doi.org/10.1148/radiol.2020201365 doi: 10.1148/radiol.2020201365
    [56] B. Xu, Y. Xing, J. Peng, Z. Zheng, W. Tang, Y. Sun, et al., Chest CT for detecting COVID-19: A systematic review and meta-analysis of diagnostic accuracy, Eur Radiol, 30 (2020), 5720–5727. https://doi.org/10.1007/s00330-020-06934-2 doi: 10.1007/s00330-020-06934-2
    [57] A. M. Rahmani, E. Azhir, M. Naserbakht, M. Mohammadi, A. H. M. Aldalwie, M. K. Majeed, et al., Automatic COVID-19 detection mechanisms and approaches from medical images: A systematic review, Multimed. Tools Appl., 81 (2022), 28779–28798. https://doi.org/10.1007/s11042-022-12952-7 doi: 10.1007/s11042-022-12952-7
    [58] E. E. Hemdan, M. A. Shouman, M. E. Karar, COVIDX-Net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images, 2020, arXiv: 2003.11055.
    [59] M. Polsinelli, L. Cinque, G. Placidi, A light CNN for detecting COVID-19 from CT scans of the chest, Pattern Recogn. Lett., 140 (2020), 95–100. https://doi.org/10.1016/j.patrec.2020.10.001 doi: 10.1016/j.patrec.2020.10.001
    [60] A. Narin, C. Kaya, Z. Pamuk, Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks, Pattern Anal, Appl., 24 (2021), 1207–1220. https://doi.org/10.1007/s10044-021-00984-y doi: 10.1007/s10044-021-00984-y
    [61] P. Mooney, Chest X-ray images (Pneumonia), 2018. Available from: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia.
    [62] T. Rahman, COVID-19 radiography database, 2020. Available from: https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database.
    [63] I. D. Apostolopoulos, T. A. Mpesiana, Covid-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks, Phys. Eng. Sci. Med., 43 (2020), 635–640. https://doi.org/10.1007/s13246-020-00865-4 doi: 10.1007/s13246-020-00865-4
    [64] P. K. Sethy, S. K. Behera, P. K. Ratha, P. Biswas, Detection of coronavirus disease (COVID-19) based on deep features and support vector machine, Int. J. Math. Eng. Manage. Sci., 5 (2020), 643–651. https://doi.org/10.33889/IJMEMS.2020.5.4.052 doi: 10.33889/IJMEMS.2020.5.4.052
    [65] Y. Wang, M. Hu, Q. Li, X. Zhang, G. Zhai, N. Yao, Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner, 2020, arXiv: 2002.05534.
    [66] J. Zhang, Y. Xie, G. Pang, Z. Liao, J. Verjans, W. Li, et al., Viral pneumonia screening on chest X-rays using confidence-aware anomaly detection, IEEE T. Med. Imaging, 40 (2021), 879–890. https://doi.org/10.1109/TMI.2020.3040950 doi: 10.1109/TMI.2020.3040950
    [67] P. Afshar, S. Heidarian, F. Naderkhani, A. Oikonomou, K. N. Plataniotis, A. Mohammadi, COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images, Pattern Recogn. Lett., 138 (2020), 638–643. https://doi.org/10.1016/j.patrec.2020.09.010 doi: 10.1016/j.patrec.2020.09.010
    [68] M. E. H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M. A. Kadir, Z. B. Mahbub, et al., Can AI help in screening viral and COVID-19 pneumonia? IEEE Access, 8 (2020), 132665–132676. https://doi.org/10.1109/ACCESS.2020.3010287 doi: 10.1109/ACCESS.2020.3010287
    [69] L. O. Hall, R. Paul, D. B. Goldgof, G. M. Goldgof, Finding Covid-19 from chest X-rays using deep learning on a small dataset, 2020, arXiv: 2004.02060.
    [70] T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, U. R. Acharya, Automated detection of COVID-19 cases using deep neural networks with X-ray images, Comput. Biol. Med., 121 (2020), 103792. https://doi.org/10.1016/j.compbiomed.2020.103792 doi: 10.1016/j.compbiomed.2020.103792
    [71] R. M. Pereira, D. Bertolini, L. O. Teixeira, C. N. Silla, Y. M. G. Costa, COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios, Comput. Meth. Prog. Bio., 194 (2020), 105532. https://doi.org/10.1016/j.cmpb.2020.105532 doi: 10.1016/j.cmpb.2020.105532
    [72] L. Mahdy, K. Ezzat, H. Elmousalami, H. Ella, A. Hassanien, Automatic X-ray COVID-19 lung image classification system based on multi-level thresholding and support vector machine, 2020, medRxiv preprint. https://doi.org/10.1101/2020.03.30.20047787
    [73] K. El Asnaoui, Y. Chawki, A. Idri, Automated methods for detection and classification pneumonia based on X-ray images using deep learning, In: Artificial intelligence and blockchain for future cybersecurity applications, Springer, Cham, 2021,257–284. https://doi.org/10.1007/978-3-030-74575-2_14
    [74] D. Singh, V. Kumar, V. Kaur, M. Kaur, Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks, Eur. J. Clin. Microbiol. Infect. Dis., 39 (2020), 1379–1389. https://doi.org/10.1007/s10096-020-03901-z doi: 10.1007/s10096-020-03901-z
    [75] M. Yamac, M. Ahishali, A. Degerli, S. Kiranyaz, M. E. H. Chowdhury, M. Gabbouj, Convolutional sparse support estimator-based COVID-19 recognition from X-ray images, IEEE T. Neur. Net. Lear. Syst., 32 (2021), 1810–1820. https://doi.org/10.1109/TNNLS.2021.3070467 doi: 10.1109/TNNLS.2021.3070467
    [76] U. Özkaya, Ş. Öztürk, M. Barstugan, Coronavirus (COVID-19) classification using deep features fusion and ranking technique, In: Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach, Springer, Cham, 2020,281–295. https://doi.org/10.1007/978-3-030-55258-9_17
    [77] C. Salvatore, M. Interlenghi, C. Monti, D. Ippolito, D. Capra, A. Cozzi, et al., Artificial intelligence applied to chest X-ray for differential diagnosis of COVID-19 pneumonia, Diagnostics, 11 (2021), 530. https://doi.org/10.3390/diagnostics11030530 doi: 10.3390/diagnostics11030530
    [78] T. T. Nguyen, Q. V. H. Nguyen, D. T. Nguyen, S. Yang, P. W. Eklund, T. Huynh-The, et al., Artificial intelligence in the battle against coronavirus (COVID-19): A survey and future research directions, 2020, arXiv: 2008.07343.
    [79] E. Neri, V. Miele, F. Coppola, R. Grassi, Use of CT and artificial intelligence in suspected or COVID-19 positive patients: Statement of the Italian society of medical and interventional radiology, La radiologia medica, 125 (2020), 505–508. https://doi.org/10.1007/s11547-020-01197-9 doi: 10.1007/s11547-020-01197-9
    [80] L. Li, L. Qin, Z. Xu, Y. Yin, X. Wang, B. Kong, et al., Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: Evaluation of the diagnostic accuracy, Radiology, 296 (2020), E65–E71. https://doi.org/10.1148/radiol.2020200905 doi: 10.1148/radiol.2020200905
    [81] M. A. Amou, K. Xia, S. Kamhi, M. Mouhafid, A novel MRI diagnosis method for brain tumor classification based on CNN and Bayesian optimization, Healthcare, 10 (2022), 494. https://doi.org/10.3390/healthcare10030494 doi: 10.3390/healthcare10030494
    [82] S. Z. Kurdi, M. H. Ali, M. M. Jaber, T. Saba, A. Rehman, R. Damaševičius, Brain tumor classification using meta-heuristic optimized convolutional neural networks, J. Pers. Med, 13 (2023), 181. https://doi.org/10.3390/jpm13020181 doi: 10.3390/jpm13020181
    [83] E. Irmak, Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework, Iran. J. Sci. Technol. Trans. Electr. Eng., 45 (2021), 1015–1036. https://doi.org/10.1007/s40998-021-00426-9 doi: 10.1007/s40998-021-00426-9
    [84] C. Venkatesh, K. Ramana, S. Y. Lakkisetty, S. S. Band, S. Agarwal, A. Mosavi, A neural network and optimization based lung cancer detection system in CT images, Front. Public Health, 10 (2022), 769692. https://doi.org/10.3389/fpubh.2022.769692 doi: 10.3389/fpubh.2022.769692
    [85] D. Paikaray, A. K. Mehta, D. A. Khan, Optimized convolutional neural network for the classification of lung cancer, J. Supercomput., 80 (2024), 1973–1989. https://doi.org/10.1007/s11227-023-05550-3 doi: 10.1007/s11227-023-05550-3
    [86] C. Lin, S. Jeng, M. Chen, Using 2D CNN with Taguchi parametric optimization for lung cancer recognition from CT images, Appl. Sci., 10 (2020), 2591. https://doi.org/10.3390/app10072591 doi: 10.3390/app10072591
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