Background Artificial intelligence (AI) has become a prominent technique for medical
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …

Automatic segmentation of brain tumour is the process of separating abnormal tissues from
normal tissues, such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) …

Abstract Medical Resonance Imaging (MRI) is one of the preferred imaging methods for
brain tumor diagnosis and getting detailed information on tumor type, location, size …

Abstract The Internet of Things (IoT) has revolutionized the medical world by facilitating data
acquisition using various IoT devices. These devices generate the data in multiple forms …

Accurate brain tumor segmentation from 3D Magnetic Resonance Imaging (3D-MRI) is an
important method for obtaining information required for diagnosis and disease therapy …

Intuitionistic fuzzy sets and rough sets are widely used for medical image segmentation, and
recently combined together to deal with uncertainty and vagueness in medical images. In …

Breast cancer has become a menacing form of cancer among women accounting for 11.6%
of total deaths of 9.6 million due to all types of cancer every year all over the world. Early …

Brain tumor segmentation in magnetic resonance imaging (MRI) is considered a complex
procedure because of the variability of tumor shapes and the complexity of determining the …

The study of brain disorders requires accurate tissue segmentation of magnetic resonance
(MR) brain images which is very important for detecting tumors, edema, and necrotic tissues …

Local region-based active contour methods have been widely used to segment images with
intensity inhomogeneity. However, this process can hardly segment images well when …