Loading [MathJax]/jax/output/SVG/jax.js
Research article Special Issues

Performance comparison of deep learning models for MRI-based brain tumor detection

  • Received: 02 November 2024 Revised: 27 December 2024 Accepted: 02 January 2025 Published: 15 January 2025
  • Brain tumors pose a significant threat to human health, as they can severely affect both physical well-being and quality of life. These tumors often lead to increased intracranial pressure and neurological complications. Traditionally, brain tumors are diagnosed through manual interpretation via medical imaging techniques such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). While these methods are effective, they are time-consuming and subject to errors in consistency and accuracy. This study explored the use of several deep-learning models, including YOLOv8, YOLOv9, Faster R-CNN, and ResNet18, for the detection of brain tumors from MR images. The results demonstrate that YOLOv9 outperforms the other models in terms of accuracy, precision, and recall, highlighting its potential as the most effective deep-learning approach for brain tumor detection.

    Citation: Abdulmajeed Alsufyani. Performance comparison of deep learning models for MRI-based brain tumor detection[J]. AIMS Bioengineering, 2025, 12(1): 1-21. doi: 10.3934/bioeng.2025001

    Related Papers:

    [1] Muajebah Hidan, Abbas Kareem Wanas, Faiz Chaseb Khudher, Gangadharan Murugusundaramoorthy, Mohamed Abdalla . Coefficient bounds for certain families of bi-Bazilevič and bi-Ozaki-close-to-convex functions. AIMS Mathematics, 2024, 9(4): 8134-8147. doi: 10.3934/math.2024395
    [2] Abdulmtalb Hussen, Mohammed S. A. Madi, Abobaker M. M. Abominjil . Bounding coefficients for certain subclasses of bi-univalent functions related to Lucas-Balancing polynomials. AIMS Mathematics, 2024, 9(7): 18034-18047. doi: 10.3934/math.2024879
    [3] Tariq Al-Hawary, Ala Amourah, Abdullah Alsoboh, Osama Ogilat, Irianto Harny, Maslina Darus . Applications of qUltraspherical polynomials to bi-univalent functions defined by qSaigo's fractional integral operators. AIMS Mathematics, 2024, 9(7): 17063-17075. doi: 10.3934/math.2024828
    [4] Abeer O. Badghaish, Abdel Moneim Y. Lashin, Amani Z. Bajamal, Fayzah A. Alshehri . A new subclass of analytic and bi-univalent functions associated with Legendre polynomials. AIMS Mathematics, 2023, 8(10): 23534-23547. doi: 10.3934/math.20231196
    [5] Bilal Khan, H. M. Srivastava, Muhammad Tahir, Maslina Darus, Qazi Zahoor Ahmad, Nazar Khan . Applications of a certain q-integral operator to the subclasses of analytic and bi-univalent functions. AIMS Mathematics, 2021, 6(1): 1024-1039. doi: 10.3934/math.2021061
    [6] Sheza. M. El-Deeb, Gangadharan Murugusundaramoorthy, Kaliyappan Vijaya, Alhanouf Alburaikan . Certain class of bi-univalent functions defined by quantum calculus operator associated with Faber polynomial. AIMS Mathematics, 2022, 7(2): 2989-3005. doi: 10.3934/math.2022165
    [7] Luminiţa-Ioana Cotîrlǎ . New classes of analytic and bi-univalent functions. AIMS Mathematics, 2021, 6(10): 10642-10651. doi: 10.3934/math.2021618
    [8] F. Müge Sakar, Arzu Akgül . Based on a family of bi-univalent functions introduced through the Faber polynomial expansions and Noor integral operator. AIMS Mathematics, 2022, 7(4): 5146-5155. doi: 10.3934/math.2022287
    [9] Norah Saud Almutairi, Adarey Saud Almutairi, Awatef Shahen, Hanan Darwish . Estimates of coefficients for bi-univalent Ma-Minda-type functions associated with q-Srivastava-Attiya operator. AIMS Mathematics, 2025, 10(3): 7269-7289. doi: 10.3934/math.2025333
    [10] Tingting Du, Zhengang Wu . Some identities involving the bi-periodic Fibonacci and Lucas polynomials. AIMS Mathematics, 2023, 8(3): 5838-5846. doi: 10.3934/math.2023294
  • Brain tumors pose a significant threat to human health, as they can severely affect both physical well-being and quality of life. These tumors often lead to increased intracranial pressure and neurological complications. Traditionally, brain tumors are diagnosed through manual interpretation via medical imaging techniques such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). While these methods are effective, they are time-consuming and subject to errors in consistency and accuracy. This study explored the use of several deep-learning models, including YOLOv8, YOLOv9, Faster R-CNN, and ResNet18, for the detection of brain tumors from MR images. The results demonstrate that YOLOv9 outperforms the other models in terms of accuracy, precision, and recall, highlighting its potential as the most effective deep-learning approach for brain tumor detection.



    Let A indicate an analytic functions family, which is normalized under the condition f (0)= f(0)1=0 in U={z:zC and |z |<1} and given by the following Taylor-Maclaurin series:

    f (z)=z+n=2anzn .      (1.1)

    Further, by S we shall denote the class of all functions in A which are univalent in U.

    With a view to recalling the principle of subordination between analytic functions, let the functions f and g be analytic in U. Then we say that the function f is subordinate to g if there exists a Schwarz function w(z), analytic in U with

    ω(0)=0, |ω(z)|<1, (zU)

    such that

    f (z)=g (ω(z)).

    We denote this subordination by

    fg or f (z)g (z).

    In particular, if the function g is univalent in U, the above subordination is equivalent to

    f (0)=g (0), f (U)g (U).

    The Koebe-One Quarter Theorem [11] asserts that image of U under every univalent function fA contains a disc of radius 14. thus every univalent function f has an inverse  f1  satisfying  f1(f(z))=z and f ( f1 (w))=w (|w|<r 0(f ),r 0(f ) >14 ), where

     f1(w)=wa2w2+(2a22a3)w3(5a325a2a3+a4)w4+. (1.2)

    A function fA is said to be bi-univalent functions in U if both f and  f1 are univalent in U. A function fS is said to be bi-univalent in U if there exists a function gS such that g(z) is an univalent extension of f1 to U. Let Λ denote the class of bi-univalent functions in U. The functions z1z, log(1z), 12log(1+z1z) are in the class Λ (see details in [20]). However, the familiar Koebe function is not bi-univalent. Lewin [17] investigated the class of bi-univalent functions Λ and obtained a bound |a2|1.51. Motivated by the work of Lewin [17], Brannan and Clunie [9] conjectured that |a2|2. The coefficient estimate problem for |an|(nN,n3) is still open ([20]). Brannan and Taha [10] also worked on certain subclasses of the bi-univalent function class Λ and obtained estimates for their initial coefficients. Various classes of bi-univalent functions were introduced and studied in recent times, the study of bi-univalent functions gained momentum mainly due to the work of Srivastava et al. [20]. Motivated by this, many researchers [1], [4,5,6,7,8], [13,14,15], [20], [21], and [27,28,29], also the references cited there in) recently investigated several interesting subclasses of the class Λ and found non-sharp estimates on the first two Taylor-Maclaurin coefficients. Recently, many researchers have been exploring bi-univalent functions, few to mention Fibonacci polynomials, Lucas polynomials, Chebyshev polynomials, Pell polynomials, Lucas–Lehmer polynomials, orthogonal polynomials and the other special polynomials and their generalizations are of great importance in a variety of branches such as physics, engineering, architecture, nature, art, number theory, combinatorics and numerical analysis. These polynomials have been studied in several papers from a theoretical point of view (see, for example, [23,24,25,26,27,28,29,30] also see references therein).

    We recall the following results relevant for our study as stated in [3].

    Let p(x) and q(x) be polynomials with real coefficients. The (p,q) Lucas polynomials Lp,q,n(x) are defined by the recurrence relation

    Lp,q,n(x)=p(x)Lp,q,n1(x)+q(x)Lp,q,n2(x)(n2),

    from which the first few Lucas polynomials can be found as

    Lp,q,0(x)=2,Lp,q,1(x)=p(x),Lp,q,2(x)=p2(x)+2q(x),Lp,q,3(x)=p3(x)+3p(x)q(x),.... (1.3)

    For the special cases of p(x) and q(x), we can get the polynomials given Lx,1,n(x)Ln(x) Lucas polynomials, L2x,1,n(x)Dn(x) Pell–Lucas polynomials, L1,2x,n(x)jn(x) Jacobsthal–Lucas polynomials, L3x,2,n(x)Fn(x) Fermat–Lucas polynomials, L2x,1,n(x)Tn(x) Chebyshev polynomials first kind.

    Lemma 1.1. [16] Let G{L(x)}(z)be the generating function of the (p,q)Lucas polynomial sequence Lp,q,n(x).Then,

    G{L(x)}(z)=n=0Lp,q,n(x)zn=2p(x)z1p(x)zq(x)z2

    and

    G{L(x)}(z)=G{L(x)}(z)1=1+n=1Lp,q,n(x)zn=1+q(x)z21p(x)zq(x)z2.

    Definition 1.2. [22] For ϑ0, δR, ϑ+iδ0 and fA, let B(ϑ,δ) denote the class of Bazilevič function if and only if

    Re[(zf(z)f(z))(f(z)z)ϑ+iδ]>0.

    Several authors have researched different subfamilies of the well-known Bazilevič functions of type ϑ from various viewpoints (see [3] and [19]). For Bazilevič functions of order ϑ+iδ, there is no much work associated with Lucas polynomials in the literature. Initiating an exploration of properties of Lucas polynomials associated with Bazilevič functions of order ϑ+iδ is the main goal of this paper. To do so, we take into account the following definitions. In this paper motivated by the very recent work of Altinkaya and Yalcin [3] (also see [18]) we define a new class B(ϑ,δ), bi-Bazilevič function of Λ based on (p,q) Lucas polynomials as below:

    Definition 1.3. For fΛ, ϑ0, δR, ϑ+iδ0 and let B(ϑ,δ) denote the class of Bi-Bazilevič functions of order t  and type ϑ+iδ if only if

    [(zf(z)f(z))(f(z)z)ϑ+iδ]G{L(x)}(z)(zU) (1.4)

    and

    [(zg(w)g(w))(g(w)w)ϑ+iδ]G{L(x)}(w)(wU), (1.5)

    where GLp,q,n(z)Φ and the function g is described as g(w)=f1(w).

    Remark 1.4. We note that for δ=0 the class R(ϑ,0)=R(ϑ) is defined by Altinkaya and Yalcin [2].

    The class B(0,0)=SΛ is defined as follows:

    Definition 1.5. A function fΛ is said to be in the class SΛ, if the following subordinations hold

    zf(z)f(z)G{L(x)}(z)(zU)

    and

    wg(w)g(w)G{L(x)}(w)(wU)

    where g(w)=f1(w).

    We begin this section by finding the estimates of the coefficients |a2| and |a3| for functions in the class B(ϑ,δ).

    Theorem 2.1. Let the function f(z) given by 1.1 be in the class B(ϑ,δ). Then

    |a2|p(x)2p(x)|{((ϑ+iδ)2+3(ϑ+iδ)+2)2(ϑ+iδ+1)2}p2(x)4q(x)(ϑ+iδ+1)2|.

    and

    |a3|p2(x)(ϑ+1)2+δ2+p(x)(ϑ+2)2+δ2.

    Proof. Let fB(ϑ,δ,x) there exist two analytic functions u,v:UU with u(0)=0=v(0), such that |u(z)|<1, |v(w)|<1, we can write from (1.4) and (1.5), we have

    [(zf(z)f(z))(f(z)z)ϑ+iδ]=G{L(x)}(z)(zU) (2.1)

    and

    [(zg(w)g(w))(g(w)w)ϑ+iδ]=G{L(x)}(w)(wU), (2.2)

    It is fairly well known that if

    |u(z)|=|u1z+u2z2+|<1

    and

    |v(w)|=|v1w+v2w2+|<1.

    then

    |uk|1and|vk|1(kN)

    It follows that, so we have

    G{L(x)}(u(z))=1+Lp,q,1(x)u(z)+Lp,q,2(x)u2(z)+=1+Lp,q,1(x)u1z+[Lp,q,1(x)u2+Lp,q,2(x)u21]z2+ (2.3)

    and

    G{L(x)}(v(w))=1+Lp,q,1(x)v(w)+Lp,q,2(x)v2(w)+=1+Lp,q,1(x)v1w+[Lp,q,1(x)v2+Lp,q,2(x)v21]w2+ (2.4)

    From the equalities (2.1) and (2.2), we obtain that

    [(zf(z)f(z))(f(z)z)ϑ+iδ]=1+Lp,q,1(x)u1z+[Lp,q,1(x)u2+Lp,q,2(x)u21]z2+, (2.5)

    and

    [(zg(w)g(w))(g(w)w)ϑ+iδ]=1+Lp,q,1(x)v1w+[Lp,q,1(x)v2+Lp,q,2(x)v21]w2+, (2.6)

    It follows from (2.5) and (2.6) that

    (ϑ+iδ+1)a2=Lp,q,1(x)u1,, (2.7)
    (ϑ+iδ1)(ϑ+iδ+2)2a22(ϑ+iδ+2)a3=Lp,q,1(x)u2+Lp,q,2(x)u21, (2.8)

    and

    (ϑ+iδ+1)a2=Lp,q,1(x)v1, (2.9)
    (ϑ+iδ+2)(ϑ+iδ+3)2a22+(ϑ+iδ+2)a3=Lp,q,1(x)v2+Lp,q,2(x)v21, (2.10)

    From (2.7) and (2.9)

    u1=v1 (2.11)

    and

    2(ϑ+iδ+1)2a22=L2p,q,1(x)(u21+v21)., (2.12)

    by adding (2.8) to (2.10), we get

    ((ϑ+iδ)2+3(ϑ+iδ)+2)a22=Lp,q,1(x)(u2+v2)+Lp,q,2(x)(u21+v21), (2.13)

    by using (2.12) in equality (2.13), we have

    [((ϑ+iδ)2+3(ϑ+iδ)+2)2Lp,q,2(x)(ϑ+iδ+1)2L2p,q,1(x)]a22=Lp,q,1(x)(u2+v2),
    a22=L3p,q,1(x)(u2+v2)[((ϑ+iδ)2+3(ϑ+iδ)+2)L2p,q,1(x)2Lp,q,2(x)(ϑ+iδ+1)2]. (2.14)

    Thus, from (1.3) and (2.14) we get

    |a2|p(x)2p(x)|{((ϑ+iδ)2+3(ϑ+iδ)+2)2(ϑ+iδ+1)2}p2(x)4q(x)(ϑ+iδ+1)2|.

    Next, in order to find the bound on |a3|, by subtracting (2.10) from (2.8), we obtain

    2(ϑ+iδ+2)a32(ϑ+iδ+2)a22=Lp,q,1(x)(u2v2)+Lp,q,2(x)(u21v21)2(ϑ+iδ+2)a3=Lp,q,1(x)(u2v2)+2(ϑ+iδ+2)a22a3=Lp,q,1(x)(u2v2)2(ϑ+iδ+2)+a22 (2.15)

    Then, in view of (2.11) and (2.12), we have from (2.15)

    a3=L2p,q,1(x)2(ϑ+iδ+2)2(u21+v21)+Lp,q,1(x)2(ϑ+iδ+2)(u2v2).
    |a3|p2(x)|ϑ+iδ+1|2+p(x)|ϑ+iδ+2|=p2(x)(ϑ+1)2+δ2+p(x)(ϑ+2)2+δ2

    This completes the proof.

    Taking δ=0, in Theorem 2.1, we get the following corollary.

    Corollary 2.2. Let the function f(z) given by (1.1) be in the class B(ϑ). Then

    |a2|p(x)2p(x)|{(ϑ2+3ϑ+2)2(ϑ+1)2}p2(x)4q(x)(ϑ+1)2|

    and

    |a3|p2(x)(ϑ+2)2+p(x)ϑ+2

    Also, taking ϑ=0 and δ=0, in Theorem 2.1, we get the results given in [18].

    Fekete-Szegö inequality is one of the famous problems related to coefficients of univalent analytic functions. It was first given by [12], the classical Fekete-Szegö inequality for the coefficients of fS is

    |a3μa22|1+2exp(2μ/(1μ)) for μ[0,1).

    As μ1, we have the elementary inequality |a3a22|1. Moreover, the coefficient functional

    ςμ(f)=a3μa22

    on the normalized analytic functions f in the unit disk U plays an important role in function theory. The problem of maximizing the absolute value of the functional ςμ(f) is called the Fekete-Szegö problem.

    In this section, we are ready to find the sharp bounds of Fekete-Szegö functional ςμ(f) defined for fB(ϑ,δ) given by (1.1).

    Theorem 3.1. Let f given by (1.1) be in the class B(ϑ,δ) and μR. Then

    |a3μa22|{p(x)(ϑ+2)2+δ2,        0|h(μ)|12(ϑ+2)2+δ22p(x)|h(μ)|,             |h(μ)|12(ϑ+2)2+δ2

    where

    h(μ)=L2p,q,1(x)(1μ)((ϑ+iδ)2+3(ϑ+iδ)+2)L2p,q,1(x)2Lp,q,2(x)(ϑ+iδ+1)2.

    Proof. From (2.14) and (2.15), we conclude that

    a3μa22=(1μ)L3p,q,1(x)(u2+v2)[((ϑ+iδ)2+3(ϑ+iδ)+2)L2p,q,1(x)2Lp,q,2(x)(ϑ+iδ+1)2]+Lp,q,1(x)2(ϑ+iδ+2)(u2v2)
    =Lp,q,1(x)[(h(μ)+12(ϑ+iδ+2))u2+(h(μ)12(ϑ+iδ+2))v2]

    where

    h(μ)=L2p,q,1(x)(1μ)((ϑ+iδ)2+3(ϑ+iδ)+2)L2p,q,1(x)2Lp,q,2(x)(ϑ+iδ+1)2.

    Then, in view of (1.3), we obtain

    |a3μa22|{p(x)(ϑ+2)2+δ2,        0|h(μ)|12(ϑ+2)2+δ22p(x)|h(μ)|,             |h(μ)|12(ϑ+2)2+δ2

    We end this section with some corollaries.

    Taking μ=1 in Theorem 3.1, we get the following corollary.

    Corollary 3.2. If fB(ϑ,δ), then

    |a3a22|p(x)(ϑ+2)2+δ2.

    Taking δ=0 in Theorem 3.1, we get the following corollary.

    Corollary 3.3. Let f given by (1.1) be in the class B(ϑ,0). Then

    |a3μa22|{p(x)ϑ+2,        0|h(μ)|12(ϑ+2)2p(x)|h(μ)|,             |h(μ)|12(ϑ+2)

    Also, taking ϑ=0, δ=0 and μ=1 in Theorem 3.1, we get the following corollary.

    Corollary 3.4. Let f given by (1.1) be in the class B. Then

    |a3a22|p(x)2.

    All authors declare no conflicts of interest in this paper.



    1 https://www.kaggle.com/datasets/pkdarabi/medical-image-dataset-brain-tumor-detection/data

    Conflict of interest



    The author declares no conflict of interest.

    [1] Aldape K, Brindle KM, Chesler L, et al. (2019) Challenges to curing primary brain tumours. Nat Rev Clin Oncol 16: 509-520. https://doi.org/10.1038/s41571-019-0177-5
    [2] Franceschi E, Frappaz D, Rudà R, et al. (2020) Rare primary central nervous system tumors in adults: an overview. Front Oncol 10: 996. https://doi.org/10.3389/fonc.2020.00996
    [3] McRobbie DW, Moore EA, Graves MJ, et al. (2017) MRI: From Picture to Proton.Cambridge University Press. https://doi.org/10.3174/ajnr.A0980
    [4] Batİ CT, Ser G (2023) Effects of data augmentation methods on YOLO v5s: application of deep learning with pytorch for individual cattle identification. Yüz Yıl Üniv Tarım Bilim Derg 33: 363-376. https://doi.org/10.29133/yyutbd.1246901
    [5] Krichen M (2023) Convolutional neural networks: a survey. Computers 12: 151. https://doi.org/10.3390/computers12080151
    [6] Muksimova S, Umirzakova S, Mardieva S, et al. (2023) Enhancing medical image denoising with innovative teacher-student model-based approaches for precision diagnostics. Sensors 23: 9502. https://doi.org/10.3390/s23239502
    [7] Rayed ME, Islam SMS, Niha SI, et al. (2024) Deep learning for medical image segmentation: state-of-the-art advancements and challenges. Inform Med Unlocked 47: 101504. https://doi.org/10.1016/j.imu.2024.101504
    [8] Huang SC, Pareek A, Seyyedi S, et al. (2020) Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. NPJ Digit Med 3: 136. https://doi.org/10.1038/s41746-020-00341-z
    [9] Abdusalomov AB, Mukhiddinov M, Whangbo TK (2023) Brain tumor detection based on deep learning approaches and magnetic resonance imaging. Cancers 15: 4172. https://doi.org/10.3390/cancers15164172
    [10] ZainEldin H, Gamel SA, El-Kenawy E-SM, et al. (2022) Brain tumor detection and classification using deep learning and sine-cosine fitness grey wolf optimization. Bioengineering 10: 18. https://doi.org/10.3390/bioengineering10010018
    [11] Passa RS, Nurmaini S, Rini DP (2023) YOLOv8 based on data augmentation for MRI brain tumor detection. Sci J Inform 10: 8. https://doi.org/10.15294/sji.v10i3.45361
    [12] Hussain M (2023) YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection. Machines 11: 677. https://doi.org/10.3390/machines11070677
    [13] Patil S, Waghule S, Waje S, et al. (2024) Efficient object detection with YOLO: a comprehensive guide. Int J Adv Res Sci Commun Technol 4: 519-531. https://doi.org/10.48175/ijarsct-18483
    [14] Alshahrani A, Mustafa J, Almatrafi M, et al. (2024) A comparative study of deep learning techniques for Alzheimer's disease detection in medical radiography. IJCSNS 24: 53-63. https://doi.org/10.22937/IJCSNS.2024.24.5.6
    [15] Kesav N, Jibukumar MG (2022) Efficient and low complex architecture for detection and classification of brain tumor using RCNN with two channel CNN. J King Saud Univ Comput Inf Sci 34: 6229-6242. https://doi.org/10.1016/j.jksuci.2021.05.008
    [16] Avşar E, Salçin K (2019) Detection and classification of brain tumours from MRI images using faster R-CNN. Teh Glas 13: 337-342. https://doi.org/10.31803/tg-20190712095507
    [17] Senan EM, Jadhav ME, Rassem TH, et al. (2022) Early diagnosis of brain tumour MRI images using hybrid techniques between deep and machine learning. Comput Math Methods Med 2022: 8330833. https://doi.org/10.1155/2022/8330833
    [18] Mathivanan SK, Sonaimuthu S, Murugesan S, et al. (2024) Employing deep learning and transfer learning for accurate brain tumor detection. Sci Rep 14: 7232. https://doi.org/10.1038/s41598-024-57970-7
    [19] El-Kenawy ESM, Khodadadi N, Mirjalili S, et al. (2024) Greylag goose optimization: nature-inspired optimization algorithm. Expert Syst Appl 238: 122147. https://doi.org/10.1016/j.eswa.2023.122147
    [20] El-Kenawy E, El-Sayed M, Rizk FH, et al. (2024) iHow optimization algorithm: a human-inspired metaheuristic approach for complex problem solving and feature selection. J Artif Intell Eng Pract 1: 36-53. https://doi.org/10.21608/jaiep.2024.386694
    [21] El-Kenawy ESM, Rizk FH, Zaki AM, et al. (2024) Football Optimization Algorithm (FbOA): a novel metaheuristic inspired by team strategy dynamics. J Artif Intell Eng Product 8: 21-38. https://doi.org/10.54216/JAIM.080103
    [22] Simo AMD, Kouanou AT, Monthe V, et al. (2024) Introducing a deep learning method for brain tumor classification using MRI data towards better performance. Inform Med Unlocked 44: 101423. https://doi.org/10.1016/j.imu.2023.101423
    [23] Ren S, He K, Girshick R, et al. (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39: 1137-1149. https://doi.org/10.1109/tpami.2016.2577031
    [24] Holzinger A, Langs G, Denk H, et al. (2019) Causability and explainability of artificial intelligence in medicine. Wiley Interdiscip Rev Data Min Knowl Discov 9: e1312. https://doi.org/10.1002/widm.1312
    [25] Edalatifar M, Bote-Curiel L, Sánchez-Cano A, et al. (2021) A hybrid neuro-fuzzy algorithm for prediction of reference impact in scientific publications. IEEE Access 9: 35784-33579. https://doi.org/10.1007/978-3-319-99834-3_31
    [26] Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement. arXiv: 180402767 . https://doi.org/10.48550/arXiv.1804.02767
    [27] Bochkovskiy A, Wang CY, Liao HM (2020) YOLOv4: optimal speed and accuracy of object detection. arXiv: 200410934 . https://doi.org/10.48550/arXiv.2004.10934
    [28] Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom 21: 6. https://doi.org/10.1186/s12864-019-6413-7
    [29] Powers DM (2011) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J Mach Learn Technol 2: 37-63. https://doi.org/10.48550/arXiv.2010.16061
    [30] Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6: 1-48. https://doi.org/10.1186/s40537-019-0197-0
    [31] Yosinski J, Clune J, Bengio Y, et al. (2014) How transferable are features in deep neural networks?. Adv Neural Inf Process Syst 27: 3320-3328. https://doi.org/10.48550/arXiv.1411.1792
    [32] Tan C, Sun F, Kong T, et al. (2018) A survey on deep transfer learning. ICANN 2018: Artificial Neural Networks and Machine Learning. Cham, Switzerland: Springer International Publishing 270-279.
    [33] Selvaraju RR, Cogswell M, Das A, et al. (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy: IEEE 618-626.
    [34] Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. arXiv: 170507874 . https://doi.org/10.48550/arXiv.1705.07874
    [35] Samek W, Wiegand T, Müller KR (2017) Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models. ITU J: ICT Discov 1: 39-48. https://doi.org/10.48550/arXiv.1708.08296
    [36] Arrieta AB, Díaz-Rodríguez N, Del Ser J, et al. (2020) Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion 58: 82-115. https://doi.org/10.1016/j.inffus.2019.12.012
    [37] Tjoa E, Guan C (2020) A survey on explainable artificial intelligence (XAI): towards medical XAI. Artif Intell Rev 53: 591-641. https://doi.org/10.1007/s10462-020-09825-y
  • This article has been cited by:

    1. Ala Amourah, Basem Aref Frasin, Thabet Abdeljawad, Sivasubramanian Srikandan, Fekete-Szegö Inequality for Analytic and Biunivalent Functions Subordinate to Gegenbauer Polynomials, 2021, 2021, 2314-8888, 1, 10.1155/2021/5574673
    2. Mohamed Illafe, Ala Amourah, Maisarah Haji Mohd, Coefficient Estimates and Fekete–Szegö Functional Inequalities for a Certain Subclass of Analytic and Bi-Univalent Functions, 2022, 11, 2075-1680, 147, 10.3390/axioms11040147
    3. Nazmiye Yilmaz, İbrahim Aktaş, On some new subclasses of bi-univalent functions defined by generalized Bivariate Fibonacci polynomial, 2022, 33, 1012-9405, 10.1007/s13370-022-00993-y
    4. Daniel Breaz, Halit Orhan, Luminiţa-Ioana Cotîrlă, Hava Arıkan, A New Subclass of Bi-Univalent Functions Defined by a Certain Integral Operator, 2023, 12, 2075-1680, 172, 10.3390/axioms12020172
    5. Luminiţa-Ioana Cotîrlǎ, Abbas Kareem Wanas, Applications of Laguerre Polynomials for Bazilevič and θ-Pseudo-Starlike Bi-Univalent Functions Associated with Sakaguchi-Type Functions, 2023, 15, 2073-8994, 406, 10.3390/sym15020406
    6. Isra Al-Shbeil, Abbas Kareem Wanas, Afis Saliu, Adriana Cătaş, Applications of Beta Negative Binomial Distribution and Laguerre Polynomials on Ozaki Bi-Close-to-Convex Functions, 2022, 11, 2075-1680, 451, 10.3390/axioms11090451
    7. Tariq Al-Hawary, Ala Amourah, Basem Aref Frasin, Fekete–Szegö inequality for bi-univalent functions by means of Horadam polynomials, 2021, 27, 1405-213X, 10.1007/s40590-021-00385-5
    8. Abbas Kareem Wanas, Luminiţa-Ioana Cotîrlă, Applications of (M,N)-Lucas Polynomials on a Certain Family of Bi-Univalent Functions, 2022, 10, 2227-7390, 595, 10.3390/math10040595
    9. Abbas Kareem Wanas, Haeder Younis Althoby, Fekete-Szegö Problem for Certain New Family of Bi-Univalent Functions, 2022, 2581-8147, 263, 10.34198/ejms.8222.263272
    10. Arzu Akgül, F. Müge Sakar, A new characterization of (P, Q)-Lucas polynomial coefficients of the bi-univalent function class associated with q-analogue of Noor integral operator, 2022, 33, 1012-9405, 10.1007/s13370-022-01016-6
    11. Tariq Al-Hawary, Coefficient bounds and Fekete–Szegö problem for qualitative subclass of bi-univalent functions, 2022, 33, 1012-9405, 10.1007/s13370-021-00934-1
    12. Ala Amourah, Basem Aref Frasin, Tamer M. Seoudy, An Application of Miller–Ross-Type Poisson Distribution on Certain Subclasses of Bi-Univalent Functions Subordinate to Gegenbauer Polynomials, 2022, 10, 2227-7390, 2462, 10.3390/math10142462
    13. Abbas Kareem Wanas, Alina Alb Lupaş, Applications of Laguerre Polynomials on a New Family of Bi-Prestarlike Functions, 2022, 14, 2073-8994, 645, 10.3390/sym14040645
    14. Ibtisam Aldawish, Basem Frasin, Ala Amourah, Bell Distribution Series Defined on Subclasses of Bi-Univalent Functions That Are Subordinate to Horadam Polynomials, 2023, 12, 2075-1680, 362, 10.3390/axioms12040362
    15. Ala Amourah, Omar Alnajar, Maslina Darus, Ala Shdouh, Osama Ogilat, Estimates for the Coefficients of Subclasses Defined by the Bell Distribution of Bi-Univalent Functions Subordinate to Gegenbauer Polynomials, 2023, 11, 2227-7390, 1799, 10.3390/math11081799
    16. Omar Alnajar, Maslina Darus, 2024, 3150, 0094-243X, 020005, 10.1063/5.0228336
    17. Muajebah Hidan, Abbas Kareem Wanas, Faiz Chaseb Khudher, Gangadharan Murugusundaramoorthy, Mohamed Abdalla, Coefficient bounds for certain families of bi-Bazilevič and bi-Ozaki-close-to-convex functions, 2024, 9, 2473-6988, 8134, 10.3934/math.2024395
    18. Ala Amourah, Ibtisam Aldawish, Basem Aref Frasin, Tariq Al-Hawary, Applications of Shell-like Curves Connected with Fibonacci Numbers, 2023, 12, 2075-1680, 639, 10.3390/axioms12070639
    19. Tariq Al-Hawary, Ala Amourah, Abdullah Alsoboh, Osama Ogilat, Irianto Harny, Maslina Darus, Applications of qUltraspherical polynomials to bi-univalent functions defined by qSaigo's fractional integral operators, 2024, 9, 2473-6988, 17063, 10.3934/math.2024828
    20. İbrahim Aktaş, Derya Hamarat, Generalized bivariate Fibonacci polynomial and two new subclasses of bi-univalent functions, 2023, 16, 1793-5571, 10.1142/S1793557123501474
    21. Abbas Kareem Wanas, Fethiye Müge Sakar, Alina Alb Lupaş, Applications Laguerre Polynomials for Families of Bi-Univalent Functions Defined with (p,q)-Wanas Operator, 2023, 12, 2075-1680, 430, 10.3390/axioms12050430
    22. Ala Amourah, Zabidin Salleh, B. A. Frasin, Muhammad Ghaffar Khan, Bakhtiar Ahmad, Subclasses of bi-univalent functions subordinate to gegenbauer polynomials, 2023, 34, 1012-9405, 10.1007/s13370-023-01082-4
    23. Tariq Al-Hawary, Basem Aref Frasin, Abbas Kareem Wanas, Georgia Irina Oros, On Rabotnov fractional exponential function for bi-univalent subclasses, 2023, 16, 1793-5571, 10.1142/S1793557123502170
    24. Tariq Al-Hawary, Ala Amourah, Hasan Almutairi, Basem Frasin, Coefficient Inequalities and Fekete–Szegö-Type Problems for Family of Bi-Univalent Functions, 2023, 15, 2073-8994, 1747, 10.3390/sym15091747
    25. Omar Alnajar, Osama Ogilat, Ala Amourah, Maslina Darus, Maryam Salem Alatawi, The Miller-Ross Poisson distribution and its applications to certain classes of bi-univalent functions related to Horadam polynomials, 2024, 10, 24058440, e28302, 10.1016/j.heliyon.2024.e28302
    26. Tariq Al-Hawary, Basem Frasin, Daniel Breaz, Luminita-Ioana Cotîrlă, Inclusive Subclasses of Bi-Univalent Functions Defined by Error Functions Subordinate to Horadam Polynomials, 2025, 17, 2073-8994, 211, 10.3390/sym17020211
  • Reader Comments
  • © 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(571) PDF downloads(38) Cited by(0)

Figures and Tables

Figures(10)  /  Tables(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog