Precision and timeliness in breast cancer detection are paramount for improving patient
outcomes. Traditional diagnostic methods have predominantly relied on unimodal …

A crucial element in the diagnosis of breast cancer is the utilization of a classification method
that is efficient, lightweight, and precise. Convolutional neural networks (CNNs) have …

Accurate and timely detection of breast cancer plays a pivotal role in reducing the mortality
rate. Deep learning models, especially CNNs, have recently shown astounding performance …

The use of artificial intelligence within the healthcare sector is consistently growing.
However, the majority of deep learning-based AI systems are of a black box nature, causing …

Breast cancer is one of the most common causes of death in women in the modern world.
Cancerous tissue detection in histopathological images relies on complex features related to …

Abstract Helicobacter pylori (H. pylori), a bacterium residing in the human stomach, can lead
to various gastric diseases, including severe gastric cancer. Microscopic examination of …

It is imperative that breast cancer is detected precisely and timely to improve patient
outcomes. Diagnostic methodologies have traditionally relied on unimodal approaches; …

In this work, we proposed a deep learning-based diagnostic system leveraging
convolutional neural networks (CNNs) to analyse histopathological images for early …

Purpose The prevalence of breast cancer continues to be a major public health concern.
Timely detection is vital for effective treatment and patient survival. In order to appropriately …

Purpose Breast cancer stands among the crucial health problems and is deemed the
second foremost reason of cancer in the world. Premature detection of malignancy in breast …