Recent years have witnessed widespread adoption of machine learning (ML)/deep learning
(DL) techniques due to their superior performance for a variety of healthcare applications …

COVID-19, an infectious disease caused by the SARS-CoV-2 virus, was declared a
pandemic by the World Health Organisation (WHO) in March 2020. By mid-August 2020 …

Huge amounts of data are circulating in the digital world in the era of the Industry 5.0
revolution. Machine learning is experiencing success in several sectors such as intelligent …

The classification of brain magnetic resonance imaging (MRI) images into normal and
abnormal classes, has great potential to reduce the radiologists workload. Statistical …

Simple Summary Radiomics could increase the value of medical images for oncologic
patients, allowing for the identification of novel imaging biomarkers and building prediction …

Accurate quantification of pulmonary nodules can greatly assist the early diagnosis of lung
cancer, enhancing patient survival possibilities. A number of nodule segmentation …

Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since
the MR signal is acquired in frequency space, any motion of the imaged object leads to …

Physiological and physical motions of the subjects, eg, patients, are the primary sources of
image artifacts in magnetic resonance imaging (MRI), causing geometric distortion, blurring …

Background Intra‐scan rigid‐body motion is a costly and ubiquitous problem in clinical
magnetic resonance imaging (MRI) of the head. Purpose State‐of‐the‐art methods for …

Data mining (DM) and machine learning (ML) applications in medical diagnostic systems
are budding. Data privacy is essential in these systems as healthcare data are highly …