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 …

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

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

Generative adversarial networks (GANs) have shown potential in learning emotional
attributes and generating new data samples. However, their performance is usually …

With the development of deep-learning techniques, the application of deep learning in MR
imaging processing seems to be growing. Accordingly, deep learning has also been …

In MRI, motion artefacts are among the most common types of artefacts. They can degrade
images and render them unusable for accurate diagnosis. Traditional methods, such as …

Speech emotion recognition (SER) is an active area of research with potential applications
in healthcare [1, 2], customer centres [3], and designing naturalistic spoken dialog systems …

Respiratory and cardiac motion can cause artifacts in magnetic resonance imaging of the
body trunk if patients cannot hold their breath or triggered acquisitions are not practical …

Radiomics in Chest CT which could be seen as a combination of the segmentation and
feature extraction steps, is not intelligible to the human operator, it is possible to determine …