Background and Objectives: Clinical diagnosis has become very significant in today's health
system. The most serious disease and the leading cause of mortality globally is brain cancer …

This paper simply presents a state of the art survey among the machine learning based
approaches for the Glioma classification. As Glioma classification is a very challenging task …

Imaging response assessment is a cornerstone of patient care and drug development in
oncology. Clinicians/clinical researchers rely on tumor imaging to estimate the impact of new …

Disease progression models are crucial to understanding degenerative diseases. Mixed-
effects models have been consistently used to model clinical assessments or biomarkers …

Abstract Interpretation of data obtained from electroencephalogram (EEG) has been
commonly used for studying the condition of the brain and to diagnose any abnormalities …

This paper plans to develop the optimal brain tumor classification model with diverse
intelligent methods. The main phases of the proposed model are '(a) image pre …

Denoising, skull stripping, segmentation, feature extraction, and classification are five
important processes in this paper's development of a brain tumor classification model. The …

Brain tumor categorization stays essential for rating tumors as well as determining treatment
choices established on their groups. To identify brain excrescences, a spread of imaging …

Purpose: Intraoperative diffusion MRI could provide a means of visualising brain fibre tracts
near a neurosurgical target after preoperative images have been invalidated by brain shift …

A brain tumor is the growth of abnormal cells in the tissues of the brain. It affects a large
number of people of different ages worldwide. Magnetic Resonance Imaging (MRI) is the …