A Diffusion-Assisted Attention U-Net++ Framework for Accurate Breast Tumor Segmentation in Ultrasound Imaging

A Diffusion-Assisted Attention U-Net++ Framework for Accurate Breast Tumor Segmentation in Ultrasound Imaging

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Accurate segmentation of breast tumors in ultrasound (US) imaging is challenging due to speckle noise, low contrast, and irregular lesion morphology. This study proposes a diffusion-assisted Attention U-Net++ framework that integrates anisotropic diffusion filtering with nested attention skip pathways to enhance noise suppression and boundary precision. The anisotropic diffusion stage reduces speckle artifacts while preserving structural edges, and the Attention U-Net++ network refines multiscale feature fusion through attention gating. Experiments were conducted on the Breast Ultrasound Images (BUSI) dataset comprising 780 images from 600 subjects. The proposed framework achieved a Dice coefficient of 0.965, IoU of 0.932, precision of 0.97, recall of 0.98 for benign lesions, and precision of 0.965, recall of 0.94 for alignant lesions, with an overall accuracy of 0.95. Grad-CAM visualization confirmed that the model focused on clinically relevant tumor regions, enhancing interpretability. The results demonstrate that combining diffusion-based preprocessing with attention-guided segmentation yields robust, efficient, and explainable performance, offering a reliable foundation for future computer-aided breast-ultrasound diagnostic systems.

Accurate segmentation of breast tumors in ultrasound (US) imaging is challenging due to speckle noise, low contrast, and irregular lesion morphology. This study proposes a diffusion-assisted Attention U-Net++ framework that integrates anisotropic diffusion filtering with nested attention skip pathways to enhance noise suppression and boundary precision. The anisotropic diffusion stage reduces speckle artifacts while preserving structural edges, and the Attention U-Net++ network refines multiscale feature fusion through attention gating. Experiments were conducted on the Breast Ultrasound Images (BUSI) dataset comprising 780 images from 600 subjects. The proposed framework achieved a Dice coefficient of 0.965, IoU of 0.932, precision of 0.97, recall of 0.98 for benign lesions, and precision of 0.965, recall of 0.94 for alignant lesions, with an overall accuracy of 0.95. Grad-CAM visualization confirmed that the model focused on clinically relevant tumor regions, enhancing interpretability. The results demonstrate that combining diffusion-based preprocessing with attention-guided segmentation yields robust, efficient, and explainable performance, offering a reliable foundation for future computer-aided breast-ultrasound diagnostic systems.

breast ultrasound imaging; Attention U-Net++; anisotropic diffusion; deep learning; tumor segmentation; Grad-CAM; computer-aided diagnosis

breast ultrasound imaging; Attention U-Net++; anisotropic diffusion; deep learning; tumor segmentation; Grad-CAM; computer-aided diagnosis

HAMZA, A.; MÉZL, M.

2026

06.02.2026

SPIE

Bellingham, Washington, USA

9781510699915

PROCEEDINGS OF SPIE Ninth International Conference on Advances in Image Processing (ICAIP 2025)

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https://www.spiedigitallibrary.org/conference-proceedings-of-spie/14017/140170E/A-diffusion-assisted-attention-U-Net-framework-for-accurate-breast/10.1117/12.3100593.full