Breast cancer is diagnosed using histopathological imaging. This task is extremely time-
consuming due to high image complexity and volume. However, it is important to facilitate …

In this paper, a feature learning enhanced convolutional neural network (FLE-CNN) is
proposed for cancer detection from histopathology images. To build a highly generalized …

Breast cancer is one of the most common invading cancers in women. Analyzing breast
cancer is nontrivial and may lead to disagreements among experts. Although deep learning …

For breast cancer diagnosis, computer-aided classification of histopathological images is of
critical importance for correct and early diagnosis. Transfer learning approaches for feature …

Across the globe, women suffer from breast cancer fatal disease. It is arising surprisingly due
to a lack of awareness among them and the inconvenient reach of diagnostic systems. Many …

Introduction and Background: Despite fast developments in the medical field, histological
diagnosis is still regarded as the benchmark in cancer diagnosis. However, the input image …

Differential Evolution (DE) is a commonly used metaheuristic algorithm in different
optimization scenarios. However, the original DE suffers from stagnation and premature …

The incidence of breast cancer is increasing rapidly around the world. Accurate
classification of the breast cancer subtype from hematoxylin and eosin images is the key to …

Researches on breast cancer histopathological image classification have achieved a great
breakthrough using deep backbones of Convolutional Neural Networks (CNNs) in recent …

Nowadays, breast cancer is the most prevalent and jeopardous disease in women after lung
cancer. During the past few decades, a substantial amount of cancer cases have been …