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Razzaq, Hasanain, Ghazali, Rozaida, George, Loay E., Zulqarnain, Muhammad. (1404). A novel framework for Breast cancer scoring based on machine learning techniques using Immunohistochemistry images. سامانه مدیریت نشریات علمی, 13(4), 1177-1188. doi: 10.22034/cmde.2024.63113.2810
Hasanain Hayder Razzaq; Rozaida Ghazali; Loay E. George; Muhammad Zulqarnain. "A novel framework for Breast cancer scoring based on machine learning techniques using Immunohistochemistry images". سامانه مدیریت نشریات علمی, 13, 4, 1404, 1177-1188. doi: 10.22034/cmde.2024.63113.2810
Razzaq, Hasanain, Ghazali, Rozaida, George, Loay E., Zulqarnain, Muhammad. (1404). 'A novel framework for Breast cancer scoring based on machine learning techniques using Immunohistochemistry images', سامانه مدیریت نشریات علمی, 13(4), pp. 1177-1188. doi: 10.22034/cmde.2024.63113.2810
Razzaq, Hasanain, Ghazali, Rozaida, George, Loay E., Zulqarnain, Muhammad. A novel framework for Breast cancer scoring based on machine learning techniques using Immunohistochemistry images. سامانه مدیریت نشریات علمی, 1404; 13(4): 1177-1188. doi: 10.22034/cmde.2024.63113.2810

A novel framework for Breast cancer scoring based on machine learning techniques using Immunohistochemistry images

Computational Methods for Differential Equations
مقاله 8، دوره 13، شماره 4، دی 2025، صفحه 1177-1188    اصل مقاله (915.9 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22034/cmde.2024.63113.2810
نویسندگان
Hasanain Hayder Razzaq* 1؛ Rozaida Ghazali2؛ Loay E. George3؛ Muhammad Zulqarnain4
11. Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, Batu Pahat, 86400, Johor, Malaysia. 2. College of Medicine, Jabir ibn Hayyan Medical University, Najaf, Iraq.
2Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, Batu Pahat, 86400, Johor, Malaysia.
3University of Information Technology and Communication (UoITC), 10001, Baghdad, Iraq.
4Department of Computer Science & IT, Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Punjab, Pakistan.
چکیده
Breast cancer is classified as a serious disease in the medical field, and there is no doubt that breast cancer detection requires effective and accurate techniques. Integrating deep learning (DL) and machine learning (ML) methods has shown promising results in this area. In this research, we introduce a hybrid approach for breast cancer diagnosis, which is centered on the analysis of immunohistochemical images. The proposed method encompasses algorithms for image pre-processing, segmentation, extracting informative indicators (such as relative cell area and intensity), and an algorithm for categorizing the molecular harmonic subtype of breast cancer. The number of the sample was 598, divided into training 70% and testing 30%. The 5-fold cross-validation was used to assess the proposed approach. Experimental results showcased the effectiveness of the proposed hybrid method in achieving superior performance in the detection of breast cancer, especially within breast cancer scoring systems. The accuracy of our proposed approach, which involved combining HSV integration with adaptive high boost filtering, reaches a peak at 96.5% when using SVC (linear kernel). Moreover, the precision, recall, F1-score, and specificity metrics are recorded at 95.29%, 99.99%, 95.59%, and 99.28%, respectively. Additionally, this study evaluated the efficacy of the proposed model in comparison to various other traditional breast cancer detection approaches.
کلیدواژه‌ها
Breast cancer؛ Immunohistochemistry؛ Segmentation؛ Enhancement؛ RGB؛ HSV
مراجع
  • [1] D. Alhajim, H. Fahama, and T. Mohammed, Enhancing and securing a real-time embedded face recognition system using Raspberry Pi, J. Al-Qadisiyah Comput. Sci. Math., 16(1) (2024), 92-104.
  • [2] W. Al-Yaseen, A. Jehad, Q. Abed, and A. Idrees, The Use of Modified K-Means Algorithm to Enhance the Performance of Support Vector Machine in Classifying Breast Cancer, Int. J. Intell. Eng. Syst., 14(2) (2021).
  • [3] M. Ashrafizadeh, A. Zarrabi, A. Bigham, A. Taheriazam, Y. Saghari, S. Mirzaei, M. Hashemi, K. Hushmandi, H. Karimi-Maleh, E. N. Zare, E. Sharifi, Y. N. Ertas, N. Rabiee, G. Sethi, and M. Shen, (Nano) platforms in breast cancer therapy: Drug/gene delivery, advanced nanocarriers and immunotherapy, Medicinal Res. Rev., 2023 (2023), 1-12.
  • [4] A. Budianto, S. Andarini, T. Hariyanti, and N. Muslihah, The prediction of colorectal cancer: Perspective of smoking and socioeconomic influence of culture in East Java with SEM analysis, Nurture, 18(2) (2024), 315-327.
  • [5] P. Chhabra and S. Goyal, A thorough review on deep learning neural network, in Proc. Int. Conf. Artif. Intell. Smart Commun. (AISC), (2023), 220-226.
  • [6] P. Donahue, A. MacKenzie, A. Filipovic, and L. Koelmeyer, Advances in the prevention and treatment of breast cancer-related lymphedema, Breast Cancer Res. Treat., 200(1) (2023), 1-14.
  • [7] P. Ghahremani, Y. Li, A. Kaufman, R. Vanguri, N. Greenwald, M. Angelo, and S. Nadeem, Deep learninginferred multiplex immunofluorescence for immunohistochemical image quantification, Nat. Mach. Intell., 4(4) (2022), 401-412.
  • [8] Global World Health Organization, Global breast cancer initiative implementation framework: Assessing, strengthening and scaling-up of services for the early detection and management of breast cancer, World Health Organization, 2023.
  • [9] N. Goodarzi, N. Chalakinia, and A. Talaei, Clarifying the Role of Emotional Self-regulation Strategies and Health Anxiety in Predicting Negative and Positive Reactions to Stress, Psychosomatic Symptoms, and Quality of Life Indicators in Women with Breast Cancer: Clarifying the role of emotional self-regulation strategies in women with breast cancer, Int. J. Body Mind Cult., 11(2), 118-126.
  • [10] Y. Gu, S. Malmir, J. Manafian, O. A. Ilhan, A. Alizadeh, and A. J. Othman, Variety interaction between k-lump and k-kink solutions for the (3+ 1)-D Burger system by bilinear analysis, Results in Physics, 43 (2022), 106032.
  • [11] G. Hiremath, J. Mathew, and N. Boraiah, Hybrid Statistical and Texture Features with DenseNet 121 for Breast Cancer Classification, Int. J. Intell. Eng. Syst., 16(2) (2023).
  • [12] D. M. Isroilovna, H. A. Altememy, A. M. Jabbar, M. A. Majeed, H. J. Jumaa, S. S. Sadeq, and et al. The Effects of Cognitive Behavioral Therapy on the Lifestyle of Breast Cancer Patients: Effects of CBT on the lifestyle of patients with breast cancer, Int J Body Mind Cult. 10(4) (2023), 531-541.
  • [13] M. Kadhim and A. Radhi, Heart disease classification using optimized machine learning algorithms, Iraqi Journal For Computer Science and Mathematics, 4(2) (2023), 3-42.
  • [14] A. A. Kassem, Snake Optimization with deep learning enabled disease detection model for colorectal cancer, Journal of Smart Internet of Things, 2022(1) (2023), 178-195.
  • [15] W. Liu, X. Chang, L. Chen, D. Phung, X. Zhang, Y. Yang, and A. Hauptmann, Pair-based uncertainty and diversity promoting early active learning for person reidentification, ACM Trans. Intell. Syst. Technol., 11(2) (2020), 1-15.
  • [16] J. Luo, H. Chen, Q. Zhang, Y. Xu, H. Huang, and X. Zhao, An improved grasshopper optimization algorithm with application to financial stress prediction, Appl. Math. Model., 64 (2018), 654-668.
  • [17] E. Mahanta, N. Hussain, N. Das, L. Kakoti, and M. Chowdhury, IHC-Net: A fully convolutional neural network for automated nuclear segmentation and ensemble classification for Allred scoring in breast pathology, Applied Soft Computing, 103(201), 107136.
  • [18] J. Manafian and M. Lakestani, Application of tan(ϕ/2)-expansion method for solving the Biswas–Milovic equation for Kerr law nonlinearity, Optik, 127(4) (2016), 2040-2054.
  • [19] J. Manafian and M. Lakestani, Optical soliton solutions for the Gerdjikov-Ivanov model via tan(ϕ/2)-expansion method, Optik, 127(20) (2016), 9603-9620.
  •  [20] J. Manafian and M. Lakestani, Abundant soliton solutions for the Kundu–Eckhaus equation via tan(ϕ/2)-expansion method, Optik, 127(14) (2016), 5543-5551.
  • [21] J. Manafian and M. Lakestani, N-lump and interaction solutions of localized waves to the (2+ 1)-dimensional variable-coefficient Caudrey–Dodd–Gibbon–Kotera–Sawada equation, Journal of Geometry and Physics, 150 (2020), 103598.
  • [22] J. Manafian, An optimal galerkin-homotopy asymptotic method applied to the nonlinear second-order bvps, Proc Inst Math Mech, 47(1) (2021), 156-182.
  • [23] M. Lakestani, J. Manafian, A. R. Najafizadeh, and M. Partohaghighi, Some new soliton solutions for the nonlinear the fifth-order integrable equations, Computational Methods for Differential Equations, 10(2) (2022), 445-460.
  • [24] J. Manafian, L. A. Dawood, and M. Lakestani, New solutions to a generalized fifth-order KdV like equation with prime number p = 3 via a generalized bilinear differential operator, Partial Differential Equations in Applied Mathematics, 9 (2024), 100600.
  • [25] J. Manafian and N. Allahverdiyeva, An analytical analysis to solve the fractional differential equations, Adv Math Models Appl, 6(2) (2021), 128-61
  • [26] V. Mathema, P. Sen, S. Lamichhane, M. Oreˇsiˇc, and S. Khoomrung,Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine, Comput. Struct. Biotechnol. J., 21 (2023), 1372-1382.
  • [27] E. Morrison, R. Lefever,N. Lewis, J. Kapadia and R. Bauer, Conventional histological and cytological staining with simultaneous immunohistochemistry enabled by invisible chromogens. Laboratory Investigation, 102(5) (2022), 545-553.
  • [28] A. Mouelhi, M. Sayadi, F. Fnaiech, K. Mrad, and K. B. Romdhane, Automatic image segmentation of nuclear stained breast tissue sections using color active contour model and an improved watershed method, Biomedical Signal Processing and Control, 8(5) (2013), 421-436.
  • [29] A. Mukhlif, B. Al-Khateeb, and M. Mohammed, Classification of breast cancer images using new transfer learning techniques, Iraqi Journal For Computer Science and Mathematics, 4(1) (2023), 167-180.
  • [30] A. Mouelhi, H. Rmili, J. Ali, M. Sayadi, R. Doghri, and K. Mrad, Fast unsupervised nuclear segmentation and classification scheme for automatic allred cancer scoring in immunohistochemical breast tissue images. Comput. Methods Programs Biomed., 165 (2018), 37-51.
  • [31] S. H. M. Mohammed, and A. C¸inar, Lung cancer classification with Convolutional Neural Network Architectures, Qubahan Academic Journal, 1(1) (2021), 33-39.
  • [32] S. K. Mohapatra, M. Abebe, L. Mekuanint, S. Prasad, P. K. Bala, and S. K. Dhala, Pneumonia and tuberculosis detection with chest x-ray images and medical records using deep learning techniques, Review of Computer Engineering Research, 10(4) (2023), 136-149.
  • [33] Y. Pang, Y. Wei, and C. Kartsonaki, Associations of adiposity and weight change with recurrence and survival in breast cancer patients: A systematic review and meta-analysis, Breast Cancer, 29(4) (2022), 575-588.
  • [34] M. M. Salih, A. M. Abdulazeez, and O. M. S. Hassan, Gender Classification Based on Iris Recognition Using Artificial Neural Networks, Qubahan Academic Journal, 1(2) (2021), 156-163.
  • [35] R. Saturi and P. Premchand, Multi-objective feature selection method by using ACO with PSO algorithm for breast cancer detection, Int. J. Intell. Eng. Syst., 14(5) (2021).
  • [36] R. Shamsi, J. Manafian, and S. Esmaeili, Ranking extreme efficient decision making units in stochastic dea, Adv. Math Models Appl, 7(1) (2022), 38-43.
  • [37] M. Talukder, M. Islam, M. Uddin, A. Akhter, M. Pramanik, S. Aryal, M. Almoyad, K. Hasan, and M. Moni, An efficient deep learning model to categorize brain tumor using reconstruction and fine-tuning, Expert Syst. Appl., 230 (2023), 120534.
  • [38] R. Torrisi, E. Marrazzo ,E Agostinetto, R. De Sanctis , A. Losurdo, G. Masci, C. Tinterri, and A. Santoro, Neoadjuvant chemotherapy in hormone receptor-positive/HER2-negative early breast cancer: When, why and what?, Critical Reviews in Oncology/Hematology, 160 (2021), 103280.
  • [39] M. Wang, H. Chen, H. Li, Z. Cai, X. Zhao, C. Tong, J. Li, and X. Xu, Grey wolf optimization evolving kernel extreme learning machine: Application to bankruptcy prediction, Eng. Appl. Artif. Intell., 63 (2017), 54-68.
  • [40] Y. Wei, N. Ni, D. Liu, H. Chen, M. Wang, Q. Li, X. Cui, and H. Ye, An improved grey wolf optimization strategy enhanced SVM and its application in predicting the second major, Math. Problems Eng., 2017 (2017), 1-12.
  • [41] X. Zhang, M. Fan, D. Wang, P. Zhou, and D. Tao, Top-k feature selection framework using robust 0-1 integer programming, IEEE Trans. Neural Netw. Learn. Syst., early access, (2020).
  • [42] X. Zhang, T. Wang, J. Wang, G. Tang, and L. Zhao, Pyramid channel-based feature attention network for image dehazing, Comput. Vis. Image Understand., 197-198 (2020), 103003.
  • [43] X. Zhang, R. Jiang, T. Wang, P. Huang, and L. Zhao, Attention-based interpolation network for video deblurring, Neurocomputing, (2020).
  • [44] X. Zhang, T. Wang, W. Luo, and P. Huang, Multi-level fusion and attention-guided CNN for image dehazing, IEEE Trans. Circuits Syst. Video Technol., early access, (2020).
  • [45] X. Zhang, R. Jiang, T. Wang, and J. Wang, Recursive neural network for video deblurring, IEEE Trans. Circuits Syst. Video Technol., Early access, (2020).
  • [46] Y. Zhang, R. Liu, A. Heidari, X. Wang, Y. Chen, M. Wang, and H. Chen, Towards augmented kernel extreme learning models for bankruptcy prediction: Algorithmic behavior and comprehensive analysis, Neurocomputing, 430 (2021), 185-212.
  • [47] M. Zhang, X. Xie, J. Manafian, O. A. Ilhan, and G. Singh, Characteristics of the new multiple rogue wave solutions to the fractional generalized CBS-BK equation, Journal of Advanced Research, 38 (2022), 131-142.
  • [48] D. Zhao, C. Huang, Y. Wei, F. Yu, M. Wang, and H. Chen, An effective computational model for bankruptcy prediction using kernel extreme learning machine approach, Comput. Econ., 49(2) (2017), 325-341.
  • [49] J. Zhou, L. Luo, Q. Dou, H. Chen, C. Chen, G. Li, Z. Jiang, and P. Heng, Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images, J. Magn. Reson. Imag., 50(4) (2019), 1144-1151.
  • [50] W. Zhu, C. Ma, X. Zhao, M. Wang, A. Heidari, H. Chen, and C. Li, Evaluation of sino foreign cooperative education project using orthogonal sine cosine optimized kernel extreme learning machine, IEEE Access, 8 (2020), 61107-61123.
  • [51] M. Zulqarnain, H. Shah, R. Ghazali, O. Alqahtani, R. Sheikh, and M. Asadullah, Attention aware deep learning approaches for an efficient stress classification model, Brain Sci., 13(7) (2023), 994.
  • [52] Y. Yang, H. Zhao, L. You, R. Tu, X. Wu, and X. Jin, Semantic portrait color transfer with internet images, Multimedia Tools Appl., 76(1) (2017), 523-541.
  • [53] C. Yu, M. Chen, K. Cheng, X. Zhao, C. Ma, F. Kuang, and H. Chen, SGOA: Annealing-behaved grasshopper optimizer for global tasks, Eng. Comput., 2 (2021), 1-28.
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