Developing an enhanced UNet-based architecture for breast tumor segmentation in ultrasound images

Ultrasound imaging is a powerful imaging modality for diagnosing breast tumors due to its non-invasive nature, real-time imaging capabilities, and lack of ionizing radiation. Ultrasound imaging has certain limitations that can make it demanding to detect masses compared to other imaging modalities. Therefore, breast ultrasound image segmentation is a crucial and challenging task in computer-aided diagnosis (CAD) systems. Deep learning (DL) has revolutionized medical image segmentation. Among DL models, UNet architecture is widely used for its exceptional performance. This study assesses the effectiveness of sharpening filters and attention mechanisms between the decoder and encoder in UNet models for breast ultrasound segmentation. Combining Sharp UNet and Attention UNet, we propose a novel approach called Parallel Sharp Attention UNet (PSA_UNet). A public dataset of 780 cases was utilized in this study. The results are promising for the proposed method, with the Dice coefficient and F1 score of 0.93 and 0.94, respectively. McNemar's results show that our proposed model outperforms the earlier designs upon which our model is based. In addition to introducing a new network, this study highlights the importance of optimization and finetuning in improving UNet-based segmentation models. The results offer potential improvements in breast cancer diagnosis and treatment planning through more accurate and efficient medical image segmentation techniques.