LKCAU-Net: A Large Kernel Coordinated Attention U-Net for Breast Tumors Segmentation in Ultrasound Images.

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Abstract

Breast cancer remains a significant health concern for women worldwide. Ultrasound imaging is widely adopted for screening due to its non-invasive and radiation-free nature. However, challenges such as low image contrast, blurred tumor boundaries, and diverse tumor morphologies severely hinder accurate segmentation. To address these issues, we propose LKCAU-Net, a novel hybrid multi-scale network that integrates Large Kernel Coordinate Attention (LKCA) and Atrous Spatial Pyramid Pooling (ASPP). The LKCA module enhances the model's ability to capture spatial details and global contextual information, while the ASPP module enhances multi-scale contextual representation. Embedded within a deep U-Net architecture, these components enable effective global-local feature fusion, significantly improving the robustness and accuracy of breast tumor segmentation in complex ultrasound images. We conducted experiments on four widely used public datasets: BUSI, Dataset B, BUSBRA and QAMEBI. For Dataset B, our proposed LKCAU-Net attained a Dice score of 0.8215, a Jaccard index of 0.7167, a precision of 0.8797, a recall of 0.7937, and a specificity of 0.9949. On the BUSI dataset, it reached a Dice score of 0.7973, Jaccard index of 0.6899, precision of 0.8320, recall of 0.7886, and specificity of 0.9825. When evaluated on the BUSBRA dataset, the model achieved a Dice score of 0.9085, Jaccard index of 0.8402, precision of 0.9206, recall of 0.9057, and specificity of 0.9964. On the QAMEBI, it reached a Dice score of 0.8815, Jaccard index of 0.8770, precision of 0.8993, recall of 0.8937, and specificity of 0.9949. Experimental results demonstrate that LKCAU-Net outperforms current state-of-the-art segmentation approaches, providing enhanced accuracy and robustness in breast cancer segmentation from ultrasound images.

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