Advancements in computing and data from the near universal acceptance and
implementation of electronic health records has been formative for the growth of …

Background and objective The high incidence of breast cancer in women has increased
significantly in the recent years. Physician experience of diagnosing and detecting breast …

Early detection of disease has become a crucial problem due to rapid population growth in
medical research in recent times. With the rapid population growth, the risk of death incurred …

Federated learning (FL) is an emerging paradigm that enables multiple organizations to
jointly train a model without revealing their private data to each other. This paper studies {\it …

Clinical datasets are commonly limited in size, thus restraining applications of Machine
Learning (ML) techniques for predictive modelling in clinical research and organ …

Breast cancer is one of the leading causes of death among women. Early detection of breast
cancer can significantly improve the lives of millions of women across the globe. Given …

Context Ensemble methods consist of combining more than one single technique to solve
the same task. This approach was designed to overcome the weaknesses of single …

The early detection of breast cancer (BC) has a significant impact on reducing the disease's
mortality rate. As an effective cost-and time-saving tool, computer-aided diagnosis (CAD) …

The application of machine learning to radiological images is an increasingly active
research area that is expected to grow in the next five to ten years. Recent advances in …

Data mining is a powerful method to extract knowledge from data. Raw data faces various
challenges that make traditional method improper for knowledge extraction. Data mining is …