Automatic classification of breast histopathology images plays a key role in computer-aided
breast cancer diagnosis. However, feature-based classification methods rely on the accurate …

Background and objective Breast cancer, the most intrusive form of cancer affecting women
globally. Next to lung cancer, breast cancer is the one that provides a greater number of …

Breast cancer is characterized by abnormal discontinuities in the lining cells of a woman's
milk duct. Large numbers of women die from breast cancer as a result of developing …

Globally, breast cancer is one of the most significant causes of death among women. Early
detection accompanied by prompt treatment can reduce the risk of death due to breast …

Breast cancer has become one of the most deadly cancer among women all over the word.
Fortunately, an early diagnosis of this type of cancer can considerably enhances the …

Breast cancer deaths are increasing rapidly due to the abnormal growth of breast cells in the
women's milk duct. Manual cancer diagnosis from mammogram images is also difficult for …

Histopathology is considered as the gold standard for diagnosing breast cancer. Traditional
machine learning (ML) algorithm provides a promising performance for cancer diagnosis if …

Early detection and diagnosis are the key factors in decreasing the breast cancer mortality
rate in medical image analysis. A randomized learning technique called extreme learning …

This paper presents a novel computer-aided diagnosis (CAD) system for the diagnosis of
breast cancer based on extreme learning machine (ELM). In view of a mammographic …

Breast tumor detection in digital mammography is one of the most important methods of
breast cancer prevention. Computer-aided diagnosis (CAD) based on extreme learning …