This study reviews the technique of convolutional neural network (CNN) applied in a specific
field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on …

Breast cancer is a great threat to females. Ultrasound imaging has been applied extensively
in diagnosis of breast cancer. Due to the poor image quality, segmentation of breast …

Breast cancer is now considered as one of the leading causes of deaths among women all
over the world. Aiming to assist clinicians in improving the accuracy of diagnostic decisions …

Breast microcalcifications are one of the important imaging features of early breast cancer
and are a key to early breast cancer diagnosis. Ultrasound imaging has been widely used in …

The main goal of this study is to build an artificial intelligence (AI) architecture for automated
extraction of dual-modal image features from both shear-wave elastography (SWE) and B …

Despite the fact that a large number of web applications are used in the medical community,
there are still certain technological challenges that need to be addressed, for example …

The rapid technology advances in medical devices make possible the generation of vast
amounts of data, which contain massive quantities of diagnostic information. Interactively …

Background The seminar-case learning (SCL) method is a case-oriented teaching model,
with teachers and students as the main body of teaching, characterized by communication …

Automatic breast ultrasound image (BUS) segmentation is still a challenging task due to
poor image quality and inherent speckle noise. In this paper, we propose a novel multi-scale …

In this paper, we present a novel computer-aided diagnostic (CAD) system based on the
Breast Imaging Reporting and Data System (BI-RADS) terminology scores of screening …