ArticleComputational intelligence and neuroscience2022
Breast Tumor Ultrasound Image Segmentation Method Based on Improved Residual U-Net Network.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis.World journal of gastroenterology · 2026Review
- Development and validation of a deep learning-based automated computed tomography image segmentation and diagnostic model for infectious hydronephrosis: a retrospective multicentre cohort study.EClinicalMedicine · 2025Article
- Research progress on artificial intelligence technology-assisted diagnosis of thyroid diseases.Frontiers in oncology · 2025Review
- DAU-Net: Dual attention-aided U-Net for segmenting tumor in breast ultrasound images.PloS one · 2024Article
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2 authors.
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Abstract
In order to achieve efficient and accurate breast tumor recognition and diagnosis, this paper proposes a breast tumor ultrasound image segmentation method based on U-Net framework, combined with residual block and attention mechanism. In this method, the residual block is introduced into U-Net network for improvement to avoid the degradation of model performance caused by the gradient disappearance and reduce the training difficulty of deep network. At the same time, considering the features of spatial and channel attention, a fusion attention mechanism is proposed to be introduced into the image analysis model to improve the ability to obtain the feature information of ultrasound images and realize the accurate recognition and extraction of breast tumors. The experimental results show that the Dice index value of the proposed method can reach 0.921, which shows excellent image segmentation performance.
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