Breast cancer analysis implies that radiologists inspect mammograms to detect suspicious
breast lesions and identify mass tumors. Artificial intelligence techniques offer automatic …

Breast cancer is one of the most common forms of cancer among women worldwide. The
development of computer-aided diagnosis (CAD) technology based on ultrasound imaging …

Breast tumor segmentation plays a crucial role in subsequent disease diagnosis, and most
algorithms need interactive prior to firstly locate tumors and perform segmentation based on …

Breast tumor segmentation and recognition from mammograms play a key role in healthcare
and treatment services. As different tumors in mammography have dissimilar densities …

In this study, we proposed a novel convolutional neural network (CNN) architecture for
classification of benign and malignant breast cancer (BC) in histological images. To improve …

The advancements in deep learning technologies have produced immense contributions to
biomedical image analysis applications. With breast cancer being the common deadliest …

Segmentation and classification of breast tumors in ultrasound (US) images is an important
application to deep neural networks. Unet architecture is recently developed for image …

Breast cancer has become a symbol of tremendous concern in the modern world, as it is one
of the major causes of cancer mortality worldwide. In this regard, breast ultrasonography …

Purpose Breast ultrasound (BUS) image segmentation plays a crucial role in computer‐
aided diagnosis systems for BUS examination, which are useful for improved accuracy of …

Breast ultrasound medical images are characterized by poor imaging quality and irregular
target edges. During the diagnosis process, it is difficult for physicians to segment tumors …