A hybrid attention network for accurate breast tumor segmentation in ultrasound images

Breast ultrasound (BUS) imaging is widely recognized as a non-invasive and cost-effective modality for the timely diagnosis of breast cancer. Despite its clinical importance, automatic tumor segmentation remains a highly challenging task because of speckle noise, varying lesion scale, and inherently indistinct boundaries between malignant and healthy tissue. To address these challenges, we introduce a novel hybrid attention-based segmentation framework, named HA-Net, tailored for BUS images. The proposed HA-Net uses a pre-trained DenseNet-121 backbone in the encoder to extract discriminative features, ensuring robustness against imaging artifacts. At the bottleneck, three complementary modules, Global Spatial Attention (GSA), Position Encoding (PE), and Scaled Dot-Product Attention (SDPA), are incorporated to capture long-range dependencies, preserve structural relationships, and model contextual interactions among features. Moreover, a Spatial Feature Enhancement Block (SFEB) is incorporated within the skip connections to refine spatial detail and emphasize tumor-relevant regions, thereby strengthening the decoder's reconstruction capability. To further improve segmentation reliability, a composite loss function is employed by combining Binary Cross-Entropy (BCE) with Jaccard Index loss, ensuring balanced optimization across pixel-level classification and region-level overlap. In comparison to current state-of-the-art (SOTA) approaches, extensive experiments on publicly available BUS datasets show that the proposed HA-Net achieves competent performance, highlighting its potential as an efficient decision-support tool for radiologists.