Numerous studies have explored different techniques for segmenting breast cancer images,
in particular deep learning-based Computer-Aided Diagnosis (CAD) has recently netted …

Breast tumors are the major malignancy in females and diagnostic systems using artificial
intelligence algorithms for breast imaging have shown promising results. Among many …

Simple Summary Early detection and personalized treatment for breast cancer are vital for
breast cancer patients survival. Computational pathology approaches can be employed by …

In the contemporary period, breast cancer has become one of the leading causes of cancer-
related mortality globally. It is referred to as asymptomatic cancer because it has no …

Mammography is one of the most effective tools radiologists use to detect breast cancer
early, as it can detect cancer up to ten years before it manifests. The accuracy of breast …

R2U-Net, or Recurrent Residual U-Net, is a U-Net extension that includes both residual and
recurrent connections for image segmentation tasks. R2U-Net is an image segmentation …

Cancer stands in second leading cause of death worldwide, an average of one in six deaths
is due to cancer. The occurrence of breast cancer is more in women compared to men …

Breast mass is one of the most distinctive signs for the diagnosis of breast cancer, and the
accurate segmentation of masses is critical for improving the accuracy of breast cancer …

In this study, we proposed a deep-learning-based semantic segmentation of breast tumors
in diagnostic breast MRI, which had been planned with chemotherapy treatment, for …

Image data in healthcare is playing a vital role. Medical data records are increasing rapidly,
which is beneficial and detrimental at the same time. Large Image dataset are difficult to …