Certain investigations on brain tumor and stroke detection techniques from mri images using machine learning approaches

Abstract

Human brain produces every action, thought, remembrance, sense and understanding of the world. The structural changes in the brain cause brain abnormality, which is considered to be important, because brain is the utmost complicated organ in the human body. The most commonly occurring brain abnormality at all ages includes tumor and stroke. The detection and analysis of brain stroke and tumor seems to be the most challenging task for neuroradiologists. In the past decades, the clinicians had only the pictures of various cross-sectional areas of the brain at the light board to diagnose the effects of the available image. Nowadays, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the most common and effective modalities used by physicians for detecting all kinds of neurological disorder. Computer Aided Diagnosis (CAD) is considered to be the most significant tool in the detection of brain abnormalities. Due to the increasing interest in the image processing field, several CAD approaches have emerged to support the medical diagnosis. However, there are some drawbacks in the existing approaches of CAD system, which is employed for the detection of brain tumor and stroke, such as lower accuracy, efficiency, robustness, reliability, computational complexity and disability to detect the severity level of the abnormality. newlineTo overcome the problems in the existing approaches of CAD system, a few novel approaches in CAD system are proposed in the current research for relatively better diagnosis and analysis of MRI brain tumor and stroke. newline