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Rising breast cancer incidence and mortality represent significant global challenges for women. Deep learning has demonstrated superior diagnostic performance in breast cancer classification compared to human experts. However, most deep learning methods ...

Breast cancer is the most prevalent kind of tumor to occur in females and the primary cause of death for women. Early detection is perhaps the most successful strategy to minimize breast cancer mortality. Early diagnosis necessitates a consistent ...

Latest studies confirmed that abnormal function of histone deacetylase (HDAC) plays a pivotal role in formation of tumors and is a potential therapeutic target for treating breast cancer. In this research, in-silico drug discovery approaches via ...

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• QSAR model was generated with good statistical results based on indazole derivatives as potential HDAC inhibitors.
• Compounds C32, C26 and C31 from model 3 had relatively higher inhibitory activity with pIC50 of 9.30103, 9.1549 and ...

The problem arises from the lack of sufficient and comprehensive information about the necessary computer techniques. These techniques are crucial for developing information systems that assist doctors in diagnosing breast cancer, especially those related ...

The Optimization‐Driven Multispectral Gamma Correction (ODMGC) algorithm overcomes challenges in gathering subtle information and detecting cancer in dense breast thermograms. This algorithm enhances the accuracy of true positives and true ...

Breast cancer is leading cause of mortality among women compared to other types of cancers. Hence, early breast cancer diagnosis is crucial to the success of treatment. Various pathological and imaging tests are available for the diagnosis of breast ...

Breast cancer continues to be an important health issue around the world, with timely screening being important in improving survival and therapy. Here is a presentation of PolyBreastVit, a novel hybrid deep learning (DL) model for the automatic detection ...

Single-cell RNA sequencing (scRNA-seq) has been invaluable in advancing our understanding of various cancers, including breast cancer. The extensive analysis of scRNA-seq data from multiple independent breast cancer studies helped build an integrated ...

The applications of centrality measures in protein-protein interaction (PPI) network analysis are diverse and encompass fundamental biological insights; cancer disease-related discoveries, and practical implications for drug development. This ...

Breast cancer (BC) continues to be a prominent issue in global public health, emphasizing the need for precise and timely detection. This paper employs a deep learning (DL) approach to introduce an extensive methodology for categorizing ...

In clinical practices, machine learning (ML) technology plays an important and rapid growing role as it is likely to help healthcare professionals making decisions and proposing new diagnoses. This research study aims in validating and comparing the ...

Breast cancer is a very common disease nowadays. But it is very important to identify and diagnose it at an early stage. So before identifying, it requires identifying and classifying the cancerous cell. Generally, in detecting cancerous cells, the '...

Breast cancer is a divergent and prominent cancer that is responsible for the morbidity and mortality of women throughout the world. This paper aims at early detection and accurate diagnosis of this fatal disease, which is one of the most ...

Molecular subtyping is a method of separating tumor clusters in a cancer type with common features according to molecular data and classification models. Genome datasets are taken from many different people and some genetic material, more precisely ...

Breast cancer is the second most common type of cancer in women in Canada and the United States, representing over 25% of all new female cancer cases. As such, there has been immense research and progress on improving screening and clinical ...

We investigate the usefulness of formula-driven supervised learning (FDSL) for breast ultrasound (US) image analysis. Medical data are usually too scarce to develop a better performing deep learning model from scratch. Transfer learning with ...

The rapid identification and accurate diagnosis of breast cancer, known as the killer of women, have become greatly significant for those patients. Numerous breast cancer histopathological image classification methods have been proposed. But they ...

Breast cancer is a worldwide public health problem, a disease that, although its risk factors are recognized by international health institutions, may vary from region to region. Knowing which risk factors are relevant for a certain type of ...

Cancer ranks as the second leading cause of mortality worldwide with breast cancer accounting for approximately 20% of all new cancer cases reported globally. Mammography is the most effective screening tool for the early diagnosis of breast ...

Digital Breast Tomosynthesis (DBT) images are widely used to increase breast cancer detection and reduce recall rates in healthcare systems for breast cancer detection. In the field of medical imaging, computer-aided diagnosis (CAD) systems are ...