The aim of this paper is to review existing approaches to the automatic detection and
segmentation of masses in mammographic images, highlighting the key-points and main …

The presented approach demonstrates an automated way of crop disease identification on
various leaf sample images corresponding to different crop species employing Local Binary …

With the exponential increase in new cases coupled with an increased mortality rate, cancer
has ranked as the second most prevalent cause of death in the world. Early detection is …

Customized deep neural networks are being used to assess medical imaging and pathology
data. The proper assessment of malignancy using digital mammography images is a …

Breast cancer is one of the prime causes of death in women. Early detection may help to
improve the survival rate to a great extent. Mammography is considered as one of the most …

We investigate the performance of six different approaches for directional feature extraction
for mass classification problem in digital mammograms. These techniques use a bank of …

This paper describes a computer-aided detection and diagnosis system for breast cancer,
the most common form of cancer among women, using mammography. The system relies on …

It is difficult for radiologists to identify the masses on a mammogram because they are
surrounded by complicated tissues. In current breast cancer screening, radiologists often …

Textural features can be useful in differentiating between benign and malignant breast
lesions on mammograms. Unlike previous computerized schemes, which relied largely on …

Breast cancer has been identified as one of the major diseases that have led to the death of
women in recent decades. Mammograms are extensively used by physicians to diagnose …