This paper provides a critical review of the literature on deep learning applications in breast
tumor diagnosis using ultrasound and mammography images. It also summarizes recent …

To investigate the effect of image preprocessing, in respect to intensity inhomogeneity
correction and noise filtering, on the robustness and reproducibility of the radiomics features …

Most medical images suffer from inadequate contrast and brightness, which leads to blurred
or weak edges (low contrast) between adjacent tissues resulting in poor segmentation and …

In developed countries, breast cancer is one of the foremost reasons for the increase in
mortality among women. Microcalcifications in breast tissue are one of the key indications …

Abstract Objective Microcalcifications or MCs are considered to be the basic symptoms
present in mammograms for breast cancer diagnosis. Therefore, the accurate detection of …

Suspicious region segmentation is one of the most important parts of CAD systems that are
used for breast cancer detection in mammograms. In a CAD system, there can be so many …

Breast cancer is a grave concern among women due to its high mortality rate in women as
compared to that in men. Mass, an early symptom of breast cancer, is difficult to detect due to …

Poor contrast and hidden details in images owing to low camera quality, bad illumination,
and poor setting for environment capture are the main challenges in the contrast …

Mammograms are essential radiological images used to diagnose breast cancer well in
advance. However, an accurate diagnosis also depends on the quality of mammogram …

Abstract We introduce Region of Interest Contrast Enhancement (RICE) to identify focal
densities in mammograms. It aims to help radiologists: 1) enhancing the contrast of …