A Multi-attention Triple Decoder Deep Convolution Network for Breast Cancer Segmentation Using Ultrasound Images

Breast cancer (BC) is a widely diagnosed deadly disease commonly present in middle-aged women around the globe. Ultrasound (U/S) imaging is widely used for the early prediction and segmentation of BC due to low radiation and cheapness. Manual BC segmentation from ultrasound imaging is a complex and laborious task due to inherited noise. Many deep learning-based breast cancer diagnostic methods are presented that can further be enhanced to improve the segmentation performance. This work proposed a U-shaped auto encoder-based multi-attention triple decoder convolution neural network for BC segmentation from U/S images. To capture multi-scale diverse spatial image features this work introduced a multi-scale convolution operation-based encoder network. To process the multi-scale learned diverse spatial features in the encoder path multi-scale triple decoder network is designed that was not found in earlier studies. To highlight the tumor region at different scales multi-attention mechanism is introduced in each decoder network. The multi-attention mechanism is designed to suppress the other region information and to highlight the tumor region features at different scales. The proposed deep network produced the segmentation dice of 90.45% on the UDIAT dataset and the segmentation dice of 89.13% on the BUSI dataset. The testing Jaccard index of 83.40% is recorded on the UDIAT dataset and a Jaccard index of 82.31% is recorded on the BUSI dataset. The result comparison with existing methods shows that our method achieved the highest results. The segmentation performance of the triple decoder-based BC segmentation model suggested that it can effectively be used to automate the manual breast cancer segmentation task from ultrasound images.