Experimental computations were performed using the computing resources of Igdir University’s Artificial Intelligence and Big Data Application and Research Center. Artificial intelligence tools were used solely to assist with language-related tasks (e.g., translation, grammar/spelling correction, and improving readability). No AI tools were used for study design, methodology, data collection, data analysis, interpretation of results, or generation of scientific content. The authors take full responsibility for the content of the manuscript.
Author contributions
SN: Conceptualization, Methodology, Investigation, Formal analysis. YC: Conceptualization, Methodology, Visualization, Writing—original draft. IP: Supervision, Project administration, Funding acquisition, Writing—review & editing. All authors read and approved the submitted version.
Conflicts of interest
The authors declare that they have no conflicts of interest.
Financial support is received from the Health Institutes of Türkiye (TÜSEB) under the “2023-C1-YZ” call, project number [33934]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Open Exploration maintains a neutral stance on jurisdictional claims in published institutional affiliations and maps. All opinions expressed in this article are the personal views of the author(s) and do not represent the stance of the editorial team or the publisher.
References
Kim J, Harper A, McCormack V, Sung H, Houssami N, Morgan E, et al. Global patterns and trends in breast cancer incidence and mortality across 185 countries.Nat Med. 2025;31:1154–62. [DOI]
Xiong X, Zheng LW, Ding Y, Chen YF, Cai YW, Wang LP, et al. Breast cancer: pathogenesis and treatments.Sig Transduct Target Ther. 2025;10:49. [DOI]
Obeagu EI, Obeagu GU. Breast cancer: A review of risk factors and diagnosis.Medicine. 2024;103:e36905. [DOI]
Kiani P, Vatankhahan H, Zare-Hoseinabadi A, Ferdosi F, Ehtiati S, Heidari P, et al. Electrochemical biosensors for early detection of breast cancer.Clinica Chimica Acta. 2025;564:119923. [DOI]
Alshawwa IA, Qasim El-Mashharawi H, Salman FM, Naji M, Al-Qumboz A, Abu-Nasser BS, et al. Advancements in Early Detection of Breast Cancer: Innovations and Future Directions.Int J Acad Eng Res. 2024;8:15–24.
Begum MM, Gupta R, Sunny B, Lutfor ZL. Advancements in Early Detection and Targeted Therapies for Breast Cancer; A Comprehensive Analysis.Asia Pac J Cancer Res. 2024;1:4–13. [DOI]
Siegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A. Cancer statistics, 2025.CA Cancer J Clin. 2025;75:10–45. [DOI]
Pacal I, Attallah O. InceptionNeXt-Transformer: A novel multi-scale deep feature learning architecture for multimodal breast cancer diagnosis.Biomed Signal Process Control. 2025;110:108116. [DOI]
Katsika L, Boureka E, Kalogiannidis I, Tsakiridis I, Tirodimos I, Lallas K, et al. Screening for Breast Cancer: A Comparative Review of Guidelines.Life. 2024;14:777. [DOI]
Trentham-Dietz A, Chapman CH, Jayasekera J, Lowry KP, Heckman-Stoddard BM, Hampton JM, et al. Collaborative Modeling to Compare Different Breast Cancer Screening Strategies: A Decision Analysis for the US Preventive Services Task Force.JAMA. 2024;331:1947–60. [DOI]
Cakmak Y, Zeynalov J. A Comparative Analysis of Convolutional Neural Network Architectures for Breast Cancer Classification from Mammograms.Artif Intell Appl Sci. 2025;1:28–34. [DOI]
Cakmak Y, Pacal N. Deep Learning for Automated Breast Cancer Detection in Ultrasound: A Comparative Study of Four CNN Architectures.Artif Intell Appl Sci. 2025;1:13–9. [DOI]
Abu Abeelh E, AbuAbeileh Z. Comparative Effectiveness of Mammography, Ultrasound, and MRI in the Detection of Breast Carcinoma in Dense Breast Tissue: A Systematic Review.Cureus. 2024;16:e59054. [DOI]
Pacal I. Chaotic Learning Rate Scheduling for Improved CNN-Based Breast Cancer Ultrasound Classification.Chaos Theory Appl. 2025;7:297–306. [DOI]
Cakmak Y, Pacal I. Comparative analysis of transformer architectures for brain tumor classification.Explor Med. 2025;6:1001377. [DOI]
Iacob R, Iacob ER, Stoicescu ER, Ghenciu DM, Cocolea DM, Constantinescu A, et al. Evaluating the Role of Breast Ultrasound in Early Detection of Breast Cancer in Low- and Middle-Income Countries: A Comprehensive Narrative Review.Bioengineering. 2024;11:262. [DOI]
Gordon PB, Warren LJ, Seely JM. Cancers Detected on Supplemental Breast Ultrasound in Women With Dense Breasts: Update From a Canadian Centre.Can Assoc Radiol J. 2025;76:497–507. [DOI]
Vogel-Minea CM, Bader W, Blohmer JU, Duda V, Eichler C, Fallenberg E, et al. Best Practice Guidelines—DEGUM Recommendations on Breast Ultrasound.Ultraschall Med. 2025;46:245–58. [DOI]
Rana AS, Rafique J, Riffat H. Advances in Breast Ultrasound Imaging: Enhancing Diagnostic Precision and Clinical Utility. London: IntechOpen; 2024. [DOI]
Pacal I, Algarni A, Kunduracioglu I. Adaptive and efficient deep learning model for automated ischemic stroke lesion segmentation.Biomed Signal Process Control. 2026;118:109658. [DOI]
Cakmak Y. Machine Learning Approaches for Enhanced Diagnosis of Hematological Disorders.Comput Syst Artif Intell. 2025;1:8–14. [DOI]
Çakmak Y, Maman A. Deep Learning for Early Diagnosis of Lung Cancer.Comput Syst Artif Intell. 2025;1:20–5. [DOI]
Pacal I. MaxCerVixT: A novel lightweight vision transformer-based Approach for precise cervical cancer detection.Knowl-Based Syst. 2024;289:111482. [DOI]
Pacal I. Investigating deep learning approaches for cervical cancer diagnosis: a focus on modern image-based models.Eur J Gynaecol Oncol. 2025;46:125–41. [DOI]
Katayama A, Aoki Y, Watanabe Y, Horiguchi J, Rakha EA, Oyama T. Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers.Int J Clin Oncol. 2024;29:1648–68. [DOI]
ABIMOULOUD ML, BENSID K, Elleuch M, Ben Ammar M, KHERALLAH M. Vision transformer-based convolutional neural network for breast cancer histopathological images classification.Multimed Tools Appl. 2024;83:86833–68. [DOI]
Balaha HM, Ali KM, Gondim D, Ghazal M, El-Baz A. Harnessing Vision Transformers for Precise and Explainable Breast Cancer Diagnosis. Cham: Springer; 2025. pp. 191–206. [DOI]
Jahan I, Chowdhury MEH, Vranic S, Al Saady RM, Kabir S, Pranto ZH, et al. Deep learning and vision transformers-based framework for breast cancer and subtype identification.Neural Comput Appl. 2025;37:9311–30.
Rahujo A, Atif D, Inam SA, Khan AA, Ullah S. A survey on the applications of transfer learning to enhance the performance of large language models in healthcare systems.Discov Artif Intell. 2025;5:90. [DOI]
Hussain S, Xi X, Ullah I, Inam SA, Naz F, Shaheed K, et al. A Discriminative Level Set Method with Deep Supervision for Breast Tumor Segmentation.Comput Biol Med. 2022;149:105995. [DOI]
Khuhro MA, Inam SA, Iqbal D, Hashim H. An Empirical Approach Towards Detection of Tuberculosis Using Deep Convolutional Neural Network.Int J Data Min Model Manag. 2024;16:101–12. [DOI]
Li M, Fang Y, Shao J, Jiang Y, Xu G, Cui X, et al. Vision transformer-based multimodal fusion network for classification of tumor malignancy on breast ultrasound: A retrospective multicenter study.Int J Med Inform. 2025;196:105793. [DOI]
Duong LT, Doan TTH, Bui AMT, Nguyen PT. Recognition of breast cancer from heterogeneous ultrasound images: A multi-level deep learning approach.Inform Med Unlocked. 2025;59:101698. [DOI]
Li H, Cheng T. Multicenter and multimodal ultrasound-based radiomics and transformer-driven end-to-end deep learning for breast cancer molecular subtype classification.J Radiat Res Appl Sci. 2025;18:101656. [DOI]
Wang Z, Wang P, Liu K, Wang P, Fu Y, Lu CT, et al. A Comprehensive Survey on Data Augmentation.arXiv:2405.09591 [Preprint]. 2024 [cited 2025 May 28]. Available from: https://arxiv.org/abs/2405.09591v3
Mumuni A, Mumuni F, Gerrar NK. A Survey of Synthetic Data Augmentation Methods in Machine Vision.Mach Intell Res. 2024;21:831–69.
Szegedy C, Vanhoucke V, Ioffe S, Shlens J. Rethinking the Inception Architecture for Computer Vision. CVPR. 2016. pp. 2818–26.
Howard A, Sandler M, Chu G, Chen LC, Chen B, Tan M, et al. Searching for MobileNetV3. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 2019. pp. 1314–24.
Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, et al. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 2021. pp. 10012–22.
Touvron H, Cord M, Douze M, Massa F, Sablayrolles A, Jégou H. Training data-efficient image transformers & distillation through attention.Proc Mach Learn Res. 2021;139:10347–57.
