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The advancements in deep learning technologies have produced immensecontributions to biomedical image analysis applications. With breast cancerbeing the common deadliest disease among women, early detection is the keymeans to improve survivability. Medical imaging like ultrasound presents anexcellent visual representation of the functioning of the organs; however, forany radiologist analysing such scans is challenging and time consuming whichdelays the diagnosis process. Although various deep learning based approachesare proposed that achieved promising results, the present article introduces anefficient residual cross-spatial attention guided inception U-Net (RCA-IUnet)model with minimal training parameters for tumor segmentation using breastultrasound imaging to further improve the segmentation performance of varyingtumor sizes. The RCA-IUnet model follows U-Net topology with residual inceptiondepth-wise separable convolution and hybrid pooling (max pooling and spectralpooling) layers. In addition, cross-spatial attention filters are added tosuppress the irrelevant features and focus on the target structure. Thesegmentation performance of the proposed model is validated on two publiclyavailable datasets using standard segmentation evaluation metrics, where itoutperformed the other state-of-the-art segmentation models.

RCA-IUnet: A residual cross-spatial attention guided inception U-Net model for tumor segmentation in breast ultrasound imaging