Breast Cancer Segmentation From Ultrasound Images Using Deep Dual-Decoder Technology With Attention Network

This paper introduces a deep learning approach for breast cancer segmentation from ultrasound imaging using a Dual Decoder Attention ResUNet (DDA-AttResUNet). DDA-AttResUNet utilizes a Dual Decoder Attention structure to simultaneously focus on tumor segmentation while also capturing supplementary contextual information, leading to enhanced segmentation accuracy. An Attention mechanism is incorporated to enhance the representation of segmented regions by effectively combining information from multiple sources. The model's performance is validated on a public challenging dataset of 780 Breast Ultrasound Images (BUSI), achieving a Dice similarity coefficient of 92.92 +/- 0.69%, Intersection over Union of 87.39 +/- 1.10%, Sensitivity of 92.16 +/- 0.92%, Precision of 93.90 +/- 0.40%, and Accuracy of 98.82 +/- 0.10%, using 10-fold cross-validation. These results, comparable to other leading methods, indicate that our DDA-AttResUNet can significantly advance breast tumor segmentation in BUS imaging, with implications for improved diagnosis and patient outcomes.