Open Access
| Issue |
BIO Web Conf.
Volume 204, 2025
International Conference on Advancing Science and Technologies in Health Science (IEM-HEALS 2025)
|
|
|---|---|---|
| Article Number | 01022 | |
| Number of page(s) | 12 | |
| DOI | https://doi.org/10.1051/bioconf/202520401022 | |
| Published online | 12 December 2025 | |
- M.A. Aslam, A. Naveed, N. Ahmed, Hybrid attention network for accurate breast tumor segmentation in ultrasound images, arXiv preprint, arXiv:2506.16592 (2025). https://doi.org/10.48550/arXiv.2506.16592 [Google Scholar]
- S. Laghmati, K. Hicham, B. Cherradi, S. Hamida, A. Tmiri, Segmentation of breast cancer on ultrasound images using attention U-Net model, Int. J. Adv. Comput. Sci. Appl., 14(8), (2023) https://doi.org/10.14569/IJACSA.2023.0140885 [Google Scholar]
- A. A. Hekal, A. Elnakib, H.E.D. Moustafa, H.M. Amer, Breast cancer segmentation from ultrasound images using deep dual-decoder technology with attention network, IEEE Access, 12, 10087–10101 (2024). https://doi/10.1109/ACCESS.2024.3351564 [Google Scholar]
- Q. He, Q. Yang, M. Xie, HCTNet: A hybrid CNN-transformer network for breast ultrasound image segmentation, Comput. Biol. Med., 155, 106629 (2023). https://doi.org/10.1016/j.compbiomed.2023.106629 [Google Scholar]
- N. Thirusangu, M. Almekkawy, Segmentation of breast ultrasound images using densely connected deep convolutional neural network and attention gates, IEEE UFFC Latin Am. Ultrason. Symp. (LAUS), 1–4 (2021). https://doi.org/10.1109/LAUS53676.2021.9639178 [Google Scholar]
- R. Almajalid, J. Shan, Y. Du, M. Zhang, Development of a deep-learning-based method for breast ultrasound image segmentation, IEEE Int. Conf. Mach. Learn. Appl. (ICMLA), 1103–1108 (2018). https://doi.org/10.1109/ICMLA.2018.00179 [Google Scholar]
- Y. Luo, Q. Huang, X. Li, Segmentation information with attention integration for classification of breast tumor in ultrasound image, Pattern Recognit., 124, 108427 (2022). https://doi.org/10.1016/j.patcog.2021.108427 [Google Scholar]
- U. Khasana, R. Sigit, H. Yuniarti, Segmentation of breast using ultrasound image for detection breast cancer, Int. Electron. Symp. (IES), 584–587 (2020). https://doi.org/10.1109/IES50839.2020.9231629 [Google Scholar]
- C. Kaushal, D. Koundal, A. Singla, Comparative analysis of segmentation techniques using histopathological images of breast cancer, Int. Conf. Comput. Methodol. Commun. (ICCMC), 261–266 (2019). https://doi.org/10.1109/ICCMC.2019.8819659 [Google Scholar]
- R. Vijayaraghavan, C. Eswari, N.R. Raajan, Analysis of ductal carcinoma using K-means clustering, Int. Conf. Electron. Commun. Syst. (ICECS), 1–4 (2014). https://doi.org/10.1109/ECS.2014.6892704 [Google Scholar]
- S. Essafi, R. Doughri, S. M’hiri, K.B. Romdhane, F. Ghorbel, Segmentation and classification of breast cancer cells in histological images, Int. Conf. Inf. Commun. Technol. (ICTTA), 1, 1097–1102 (2006). https://doi.org/10.1109/ICTTA.2006.1684527 [Google Scholar]
- R. Ranjbarzadeh, S. Dorosti, S.J. Ghoushchi, A. Caputo, E.B. Tirkolaee, S.S. Ali, M. Bendechache, Breast tumor localization and segmentation using machine learning techniques: overview of datasets, findings, and methods, Comput. Biol. Med., 152, 106443 (2023). https://doi.org/10.1016/j.compbiomed.2022.106443 [Google Scholar]
- Y.M. George, B.M. Bagoury, H.H. Zayed, M.I. Roushdy, Automated cell nuclei segmentation for breast fine needle aspiration cytology, Signal Process., 93(10), 2804–2816 (2013). https://doi.org/10.1016/j.sigpro.2012.07.034 [Google Scholar]
- E. Michael, H. Ma, H. Li, F. Kulwa, J. Li, Breast cancer segmentation methods: current status and future potentials, Biomed. Res. Int., 2021(1), 9962109 (2021). https://doi.org/10.1155/2021/9962109 [Google Scholar]
- Y. Zeng, A novel deep learning approach for breast cancer ultrasound image segmentation, Theor. Nat. Sci., 99(1), 199–207 (2025). https://doi.org/10.54254/2753-8818/2025.23012 [Google Scholar]
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