Brain Tumor Detection and Segmentation: A Survey

Abstract

<p class="Abstract">Even with the enormous progress in medical technology, brain tumor detection is still an extremely tedious and complex task for the physicians. The early and accurate detection of brain tumors enables effective and efficient therapy and thus can result in increased survival rates. Automatic detection and classification of brain tumors have the potential to achieve efficiency and a higher degree of predictable accuracy. However, it is well established that the accuracy performance of automatic detection and classification techniques varies from technique to technique, and tends to be image modality dependent. This paper reviews the state-of-the-art detection techniques and highlights their pros and cons.</p>

A Systematic Review on Brain Mri Classification

“…Figure 1 shows the central block diagram of the categorization of brain tumors. The main steps in the figure involve preprocessing and segmenting medical MRI images, extracting characteristics and selecting the most crucial ones, and afterward using machine learning algorithms [6]. The following subsections provide a detailed explanation of each stage.…”

“…The characteristics of the components that make up the human body, such as water, bones, iodine, iron, and others, affect the technology and organizing ideas of medical imaging [7]. X-rays, computed tomography (CT), positron emission tomography (PET), ultrasound, and magnetic resonance imaging are examples of imaging techniques (MRI).…”