1. Wen X, Guo X, Wang S, Lu Z, Zhang Y. Breast cancer diagnosis: A systematic review. Biocybern Biomed Eng. 2024; 44:119-48. doi: 10.1016/j.bbe.2024.01.002.

2. Priya CV L, VG B, BR V, Ramachandran S. Deep learning approaches for breast cancer detection in histopathology images: A review. Cancer Biomark. 2024; 40:1-25. doi: 10.3233/CBM-230251.

3. Saroğlu HE, et al. Machine learning, IoT and 5G technologies for breast cancer studies: A review. Alex Eng J. 2024; 89:210-23. doi: 10.1016/j.aej.2024.01.043.

4. Ciobotaru A, Corches C, Gota D, Miclea L. Deep Learning and Federated Learning in Breast Cancer Screening and Diagnosis: A Systematic Review. IEEE Access. 2025;13: 76322-51. doi: 10.1109/ACCESS.2025.3560211.

5. Gurmessa D kaba, Jimma W. Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review. BMJ Health Care Inform. 2024;31: e100954. doi: 10.1136/bmjhci-2023-100954.

6. Arebi P, Fatemi A, Ramezani R. Event stream controllability on event-based complex networks. Expert Syst Appl. 2023; 213:118886. doi: 10.1016/j.eswa.2022.118886.

7. Fernández-Arjona M del M, Grondona JM, Granados-Durán P, Fernández-Llebrez P, López-Ávalos MD. Microglia morphological categorization in a rat model of neuroinflammation by hierarchical cluster and principal components analysis. Front Cell Neurosci. 2017; 11:235. doi: 10.3389/fncel.2017.00235.

8. Jha S, Khalf MF, et al. Convolutional neural networks for breast cancer detection using regions of interest from infrared images. TJHEST. 2023; 1:52-61. doi: 10.59785/tjhest. v1i2.25.

9. Li H, Tian Z, Li X, Arebi P. Identifying effective nodes in term of the influence maximization on the social IoT networks using deep learning-based node embedding. Computing. 2025;107: 1-34.doi: 10.1007/s00607-024-01376-w.

10. Kwessi E, Edwards LJ. Artificial neural networks with a signed-rank objective function and applications. Commun Stat Simul Comput. 2022; 51:3363-88. doi:10.1080/03610918.2020.1714659

11. Collier ZK, Leite WL, Zhang H. Estimating propensity scores using neural networks and traditional methods: a comparative simulation study. Commun Stat Simul Comput. 2023;52: 4545-60.doi: 10.1080/03610918.2021.1963455.

12. Chowdhury RI, Hasan MT, Huda S, Sneddon G. Regressive class models for machine learning algorithms to predict trajectories of repeated multinomial outcomes: an application to the activity of daily living of elderly data. Commun Stat Simul Comput. 2025; 54:2110-31. doi: 10.1080/03610918.2024.2311774.

13. Deng W, Deng J, Arebi P. Detection of Effective Devices in Information Dissemination on the Complex Social Internet of Things Networks Based on Device Centrality Measures. Complexity. 2025; 2025:2919169. doi: 10.1155/cplx/2919169.

14. Abunasser BS, Daud SM, Zaqout I, Abu-Naser SS. Convolution neural network for breast cancer detection and classification-final results. J Theor Appl Inf Technol. 2023; 101:315-29. doi: 10.31557/APJCP.2023.24.2.531.

15. Abdelrahman L, Al Ghamdi M, Collado-Mesa F, Abdel-Mottaleb M. Convolutional neural networks for breast cancer detection in mammography: A survey. Comput Biol Med. 2021; 131:104248. doi: 10.1016/j.compbiomed.2021.104248.

16. Ahmad W, Wang B, Chen S. Learning to rank influential nodes in complex networks via convolutional neural networks. Appl Intell. 2024; 54:3260-78. doi: 10.1007/s10489-024-05336-x

17. Lou Y, He Y, Wang L, Chen G. Predicting network controllability robustness: A convolutional neural network approach. IEEE Trans Cybern. 2020;52: 4052-63. doi: 10.1109/TCYB.2020.3013251.

18. Tanveer H, Faheem M, Khan AH, Adam MA. AI-Powered Diagnosis: A Machine Learning Approach to Early Detection of Breast Cancer. IJEIR. 2025; 13:153-66.

19. Fatima A, et al. Analyzing breast cancer detection using machine learning & deep learning techniques. JCBI. 2024; 7.doi: 0.56979/702/2024.

20. Wekalao J. Enhanced terahertz graphene metasurface biosensor for early breast cancer detection with machine learning optimization based on locally weighted linear regression. Plasmonics. 2026; 21:349-61. doi: 10.1007/s11468-025-02905-9.

21. Ahmad J, Akram S, Jaffar A, Ali Z, Bhatti SM, Ahmad A, et al. Deep learning empowered breast cancer diagnosis: Advancements in detection and classification. PLoS One. 2024;19: e0304757. doi: 10.1371/journal.pone.0304757.

22. Rahman MM, et al. Breast cancer detection and localizing the mass area using deep learning. Big Data Cogn Comput. 2024; 8:80. doi: 10.3390/bdcc8070080.

23. Wang L. Mammography with deep learning for breast cancer detection. Front Oncol. 2024; 14:1281922. doi: 10.3389/fonc.2024.1281922.

24. Kanber BM, Al Smadi A, Noaman NF, Liu B, Gou S, Alsmadi MK. Lightgbm: A leading force in breast cancer diagnosis through machine learning and image processing. IEEE Access. 2024; 12:39811-32. doi: 10.1109/ACCESS.2024.3375755.

25. Zeng R, et al. FastLeakyResNet-CIR: A novel deep learning framework for breast cancer detection and classification. IEEE Access. 2024;12: 70825-70832. doi: 10.1109/ACCESS.2024.3401729.

26. Mahmood T, Saba T, Rehman A, Alamri FS. Harnessing the power of radiomics and deep learning for improved breast cancer diagnosis with multiparametric breast mammography. Expert Syst Appl. 2024; 249:123747. doi: 10.1016/j.eswa.2024.123747.

27. Al-Imran M, et al. Evaluating Machine Learning Algorithms for Breast Cancer Detection: A Study on Accuracy and Predictive Performance. TAJET. 2024; 6:22-33. doi: 10.37547/tajet/Volume06Issue09-04.

28. Kavitha N, Madhumathy P, Prasad RM, Chandrappa DN. Machine learning technique for breast cancer detection and classification. Mach Learn Comput Sci Eng. 2025; 1:16. doi: 10.1007/s44379-025-00018-y.

29. Yaqoob A, Verma NK, Aziz RM, Shah MA. RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques. J Cancer Res Clin Oncol. 2024; 150:455. doi: 10.1007/s00432-024-05968-z.

30. Jabeen K, et al. An intelligent healthcare framework for breast cancer diagnosis based on the information fusion of novel deep learning architectures and improved optimization algorithm. Eng Appl Artif Intell. 2024; 137:109152. doi: 10.1016/j.engappai.2024.109152.

31. Zaylaa AJ, Kourtian S. Advancing breast cancer diagnosis through breast mass images, machine learning, and regression models. Sensors. 2024; 24:2312. doi: 10.3390/s24072312.

32. Guo D, et al. A multimodal breast cancer diagnosis method based on Knowledge-Augmented Deep Learning. Biomed Signal Process Control. 2024; 90: 105843. doi: 10.1016/j.bspc.2023.105843.

33. Teoh JR, Hasikin K, Lai KW, Wu X, Li C. Enhancing early breast cancer diagnosis through automated microcalcification detection using an optimized ensemble deep learning framework. Peer J Comput Sci. 2024;10: e2082. doi: 10.7717/peerj-cs.2082.

34. Shah D, Ullah Khan MA, Abrar M. Reliable Breast Cancer Diagnosis with Deep Learning: DCGAN-Driven Mammogram Synthesis and Validity Assessment. Appl. Comput Intell Soft Comput. 2024; 2024:1122109. doi: 10.1155/2024/1122109.

35. Kebede SR, et al. Dual view deep learning for enhanced breast cancer screening using mammography. Sci Rep. 2024; 14:3839. doi: 10.1038/s41598-023-50797-8.

36. Yadav AR, Kumar VN. PSO-optimized fractional order CNNs for enhanced breast cancer detection. Results Eng. 2025; 26:104559. doi: 10.1016/j.rineng.2025.104559.

37. Kumar R, Chaudhry M, Patel HK, Prakash N, Dogra A, Kumar S. An analysis of ensemble machine learning algorithms for breast cancer detection: Performance and Generalization," 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 2024, pp. 366-370, doi: 10.23919/INDIACom61295.2024.10498618.

38. Zarif S, Abdulkader H, Elaraby I, Alharbi A, Elkilani WS, Pławiak P. Using hybrid pre-trained models for breast cancer detection. PLoS One. 2024;19: e0296912. doi: 10.1371/journal.pone.0296912.

39. Carriero A, Groenhoff L, Vologina E, Basile P, Albera M. Deep learning in breast cancer imaging: State of the art and recent advancements in early 2024. Diagnostics. 2024; 14:848. doi: 10.3390/diagnostics14080848.

40. Priyadarshni V, Sharma SK, Rahmani MKI, Kaushik B, Almajalid R. Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection. Comput Mater Contin. 2024;78. doi: 10.32604/cmc.2024.044963.

41. Abunasser BS, Al-Hiealy MRJ, Zaqout IS, Abu-Naser SS. Convolution neural network for breast cancer detection and classification using deep learning. Asian Pac J Cancer Prev. 2023; 24:531-44. doi: 10.31557/APJCP.2023.24.2.531.

42. Desai M, Shah M. An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clin eHealth. 2021; 4:1-11. doi: 10.1016/j.ceh.2020.11.002.

43. Oza P, Sharma P, Patel S, Kumar P. Deep convolutional neural networks for computer-aided breast cancer diagnostic: a survey. Neural Comput Appl. 2022; 34:1815-36. doi: 10.1007/s00521-021-06804-y

44. Das HS, Das A, Neog A, Mallik S, Bora K, Zhao Z. Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks-based approach. Front Genet. 2023; 13:1097207. doi: 10.3389/fgene.2022.1097207.

45. Naz A, Khan H, Din IU, Ali A, Husain M. An Efficient Optimization System for Early Breast Cancer Diagnosis based on Internet of Medical Things and Deep Learning. Eng Technol Appl Sci Res. 2024; 14:15957-62. doi: 10.48084/etasr.8080.

46. Sahu A, Das PK, Meher S. An efficient deep learning scheme to detect breast cancer using mammogram and ultrasound breast images. Biomed Signal Process Control. 2024; 87:105377. doi: 10.1016/j.bspc.2023.105377.

47. Qureshi SA, et al. Breast Cancer Detection using Mammography: Image Processing to Deep Learning. IEEE Access 2025;13: 60776-801. doi: 10.1109/ACCESS.2024.3523745.