The incidence and mortality rate of Breast Cancer (BC) are global problems for women, with
over 2.1 million new diagnoses each year worldwide. There is no age range, race, or …

Abstract Transparency in Machine Learning (ML), often also referred to as interpretability or
explainability, attempts to reveal the working mechanisms of complex models. From a …

Deep neural networks (DNN) have remarkably progressed in applications involving large
and complex datasets but have been criticized as a black-box. This downside has recently …

Breast mass identification is a crucial procedure during mammogram-based early breast
cancer diagnosis. However, it is difficult to determine whether a breast lump is benign or …

With well-defined graphs, graph convolution based spatiotemporal neural networks for traffic
prediction have achieved great performance in numerous tasks. Compared to other …

Breast cancer exhibits one of the highest incidence and mortality rates among all cancers
affecting women. The early detection of breast cancer reduces mortality and is crucial for …

Deep neuro-fuzzy systems (DNFSs) have been successfully applied to real-world problems
using the efficient learning process of deep neural networks (DNNs) and reasoning aptitude …

Breast cancer is one of the most common diseases in women; it can have long-term
implications and can even be fatal. However, early detection, achieved through recent …

Breast ultrasound images segmentation is one of the key steps in clinical auxiliary diagnosis
of breast cancer, which seriously threatens women's health. Currently, deep learning …

Machine learning draws its power from various disciplines, including computer science,
cognitive science, and statistics. Although machine learning has achieved great …