Adaptive fuzzy deformable fusion and optimized CNN with ensemble classification for automated brain tumor diagnosis.

Automatic classification of brain tumor plays a vital role to speed up the treatment procedure, plan and boost the survival rate of patients. Nowadays, Magnetic Resonance Imaging (MRI) is employed for determining brain tumor. However, manual identification of brain tumor is purely based on the sensitivity and experience of medical professionals.

A Hybrid Approach for Sub-Acute Ischemic Stroke Lesion Segmentation Using Random Decision Forest and Gravitational Search Algorithm.

Background: The sub-acute ischemic stroke is the most basic illnesses reason for death on the planet. We evaluate the impact of segmentation technique during the time of breaking down the capacities of the cerebrum.

Objective: The main objective of this paper is to segment the ischemic stroke lesions in Magnetic Resonance (MR) images in the presence of other pathologies like neurological disorder, encephalopathy, brain damage, Multiple sclerosis (MS).