The optimal segmentation of medical images remains important for promoting the intensive
use of automatic approaches in decision making, disease diagnosis, and facilitating the …

During the last decades, the healthcare area has increasingly relied on medical imaging for
the diagnosis of a growing number of pathologies. The different types of medical images are …

Image segmentation techniques have been an invaluable task in many domains such as
quantification of tissue volumes, medical diagnosis, anatomical structure study, treatment …

According to functional or anatomical modalities, medical imaging provides a visual
representation of complex structures or activities in the human body. One of the most …

Recent improvements in current technology have had a significant impact on a wide range
of image processing applications, including medical imaging. Classification, detection, and …

Many image segmentation solutions are problem-based. Medical images have very similar
grey level and texture among the interested objects. Therefore, medical image segmentation …

In recent years, deep learning has rapidly become a method of choice for segmentation of
medical images. Deep neural architectures such as UNet and FPN have achieved high …

Image segmentation technology has made a remarkable effect in medical image analysis
and processing, which is used to help physicians get a more accurate diagnosis. Manual …

Artificial intelligence is the outlet of computer science apprehensive with creating computers
that perform as humans. It compromises expert systems, playing games, natural language …

This study proposes an umbrella deployment of swarm intelligence algorithm, such as
stochastic diffusion search for medical imaging applications. After summarising the results of …