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An Improved U-Net Model for Ultrasound Image Segmentation of Breast Cancer
- 1 School of Management and Engineering, Capital University of Economics and Business, No. 121, Zhangjialukou, Huaxiang, Fengtai District, Beijing, 100070, China
* Author to whom correspondence should be addressed.
Abstract
Breast cancer is the most common and one of the most lethal malignant tumors among women worldwide. Early and accurate diagnosis plays a crucial role in improving patient survival rates. As one of the primary imaging modalities, breast ultrasound imaging has been widely employed in clinical screening due to its low cost and lack of radiation exposure. However, limited by its imaging mechanism, ultrasound images often suffer from severe speckle noise interference, blurred boundaries, and complex tissue structures, which significantly hinder the performance of automatic lesion segmentation. To address this challenge, this paper proposes an improved Attention U-Net model. By introducing Attention Gate modules into the conventional U-Net architecture, the model is guided to focus on salient regions associated with lesions while suppressing background interference. Moreover, the network depth is increased to enhance feature representation capabilities. As a result, the proposed model achieves improved segmentation accuracy and boundary fitting performance in complex scenarios.
Keywords
breast cancer, ultrasound imaging, image segmentation, U-Net, attention mechanism, deep learning
[1]. M. Arnold et al., “Current and future burden of breast cancer: Global statistics for 2020 and 2040,” The Breast, vol. 66, pp. 15–23, Dec. 2022, doi: 10.1016/j.breast.2022.08.010.
[2]. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, Eds., Cham: Springer International Publishing, 2015, pp. 234–241. doi: 10.1007/978-3-319-24574-4_28.
[3]. J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3431–3440. Accessed: Mar. 29, 2025. [Online]. Available: https://openaccess.thecvf.com/content_cvpr_2015/html/Long_Fully_Convolutional_Networks_2015_CVPR_paper.html
[4]. W. Al-Dhabyani, M. Gomaa, H. Khaled, and A. Fahmy, “Dataset of breast ultrasound images,” Data in Brief, vol. 28, p. 104863, Feb. 2020, doi: 10.1016/j.dib.2019.104863.
[5]. K. Han et al., “A Survey on Vision Transformer,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 87–110, Jan. 2023, doi: 10.1109/TPAMI.2022.3152247.
[6]. Z. Liu et al., “Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows,” presented at the Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 10012–10022. Accessed: Mar. 29, 2025. [Online]. Available: https://openaccess.thecvf.com/content/ICCV2021/html/Liu_Swin_Transformer_Hierarchical_Vision_Transformer_Using_Shifted_Windows_ICCV_2021_paper
[7]. A. Kirillov et al., “Segment Anything,” presented at the Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 4015–4026. Accessed: Mar. 29, 2025. [Online]. Available: https://openaccess.thecvf.com/content/ICCV2023/html/Kirillov_Segment_Anything_ICCV_2023_paper.html
[8]. K. Weiss, T. M. Khoshgoftaar, and D. Wang, “A survey of transfer learning,” J Big Data, vol. 3, no. 1, p. 9, May 2016, doi: 10.1186/s40537-016-0043-6.
[9]. Z. Li et al., “Cross-modality representation and multi-sample integration of spatially resolved omics data,” Jun. 11, 2024, bioRxiv. doi: 10.1101/2024.06.10.598155.
[10]. R. Krishnan, P. Rajpurkar, and E. J. Topol, “Self-supervised learning in medicine and healthcare,” Nat. Biomed. Eng, vol. 6, no. 12, pp. 1346–1352, Dec. 2022, doi: 10.1038/s41551-022-00914-1.
Cite this article
Guo,P. (2025). An Improved U-Net Model for Ultrasound Image Segmentation of Breast Cancer. Applied and Computational Engineering,150,27-34.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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