Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases,
such as skin, breast, and lung cancer. AI is an advanced form of technology that uses …

Computer‐aided diagnosis (CAD) has been a major field of research for the past few
decades. CAD uses machine learning methods to analyze imaging and/or nonimaging …

Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …

In medical imaging, segmentation plays a vital role towards the interpretation of X-ray
images where salient features are extracted with the help of image segmentation. Without …

The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered
data with nuanced decision making. Cancer offers a unique context for medical decisions …

The use of machine learning (ML) has been increasing rapidly in the medical imaging field,
including computer-aided diagnosis (CAD), radiomics, and medical image analysis …

Advances in both imaging and computers have synergistically led to a rapid rise in the
potential use of artificial intelligence in various radiological imaging tasks, such as risk …

Training a deep convolutional neural network (CNN) from scratch is difficult because it
requires a large amount of labeled training data and a great deal of expertise to ensure …

Deep learning is the state-of-the-art machine learning approach. The success of deep
learning in many pattern recognition applications has brought excitement and high …