Vol. 7, Issue 5, Part E (2021)

Abstract
An automated neurological disorder identification system that uses computer vision on magnetic resonance imaging to locate brain tumors (MRI). The most common and dangerous form of brain cancer is gliomas. Gliomas are tumors that, at their most advanced stage, result in a much shorter life span. Preparing for therapy is an important step in maintaining a better quality of life for oncology patients. Magnetic resonance imaging (MRI) is a technique for examining the structures and components of the human body as well as for medical diagnosis, determining the stage of disease, and monitoring without the use of ionizing radiation. The significant spatial and structural changeability of brain tumors complicates segmentation. As a result, an automatic and consistent segmentation technique is used, based on Convolutional Neural Networks (CNN). Because of the relatively low number of network weights, the use of small kernels enables the development of a deeper architecture, with such a positive effect on over-fitting. It also explores the use of intensity normalization as a pre-processing phase, which is not widely used in segmentation techniques based on the Convolution Neural Network, but has been shown to be efficient in segmenting brain tumors using Magnetic Resonance Imaging (MRI) in conjunction with data augmentation.