  The advancements in deep learning technologies have produced immense
contributions to biomedical image analysis applications. With breast cancer
being the common deadliest disease among women, early detection is the key
means to improve survivability. Medical imaging like ultrasound presents an
excellent visual representation of the functioning of the organs; however, for
any radiologist analysing such scans is challenging and time consuming which
delays the diagnosis process. Although various deep learning based approaches
are proposed that achieved promising results, the present article introduces an
efficient residual cross-spatial attention guided inception U-Net (RCA-IUnet)
model with minimal training parameters for tumor segmentation using breast
ultrasound imaging to further improve the segmentation performance of varying
tumor sizes. The RCA-IUnet model follows U-Net topology with residual inception
depth-wise separable convolution and hybrid pooling (max pooling and spectral
pooling) layers. In addition, cross-spatial attention filters are added to
suppress the irrelevant features and focus on the target structure. The
segmentation performance of the proposed model is validated on two publicly
available datasets using standard segmentation evaluation metrics, where it
outperformed the other state-of-the-art segmentation models.
