In the present paper, a hybrid multilevel thresholding technique that combines intuitionistic
fuzzy sets and tsallis entropy has been proposed for the automatic delineation of the tumor …

Brain extraction is a process of removing non-brain tissue in the brain magnetic resonance
(MR) images and serves as a first step towards more delicate brain segmentation. Although …

The present chapter proposes an automatic segmentation method that performs multilevel
image thresholding by using the spatial information encoded in the gray level co-occurrence …

Internet of medical things (IoMT) is becoming more prevalent in healthcare applications as a
result of current AI advancements, helping to improve our quality of life and ensure a …

The massive amount of human biological, imaging, and clinical data produced by multiple
and diverse sources necessitates integrative modeling approaches able to summarize all …

Researchers have typically studied the brain by monitoring characteristic signals, such as
electrophysiology and neurotransmitters by implanted electronics. Here, real‐time …

Deep learning (DL) along with never-ending advancements in computational processing
and cloud technologies have bestowed us powerful analyzing tools and techniques in the …

Glioma, a primary tumor derived from brain glial cells, is around 45% of all intracranial
tumors. Magnetic resonance imaging's (MRI's) precise glioma segmentation is crucial for …

Tumors are normally classified by analyzing the magnetic resonance imaging (MRI) of the
human brain. The proposed work has developed a programmed machine-based tumor …

It is difficult to accurately segment brain MRI in the complex structures of brain tumors,
blurred borders, and external variables such as noise. Much research in developing as well …