European Journal of Molecular & Clinical Medicine ISSN 2515-8260 Volume 08, Issue 03, 2021 215 BRAIN MRI ANALYSIS AND SEGMENTATION USING 2D-UNET ARCHITECTURE Angelin Beulah. S Research Scholar, School of Computer Science and Engineering (SCOPE), Vellore Institute Technology, Chennai 600 127, India angelinbeulah.s2018@vitstudent.ac.in Kartikay Kaul Student, School of Computer Science and Engineering (SCOPE), Vellore Institute Technology, Chennai 600 127, India kartikaykaul13@gmail.com Daksh Chauhan Student, School of Computer Science and Engineering (SCOPE), Vellore Institute Technology, Chennai 600 127, India daskshjaraik172@gmail.com Hepsiba Mabel.V Associate Professor, School of Computer Science and Engineering (SCOPE), Vellore Institute Technology, Chennai 600 127, India ( * Corresponding author’s e-mail: angelinbeulah.s2018@vitstudent.ac.in) Abstract: Deep Neural Networks have demonstrated amazingly positive execution in the field of computer vision issues - object acknowledgment, discovery, and division. These techniques have been used in the clinical picture examination area. Convolutional neural systems (CNNs), a remarkable part of profound learning applications to visual purposes, have earned significant consideration in the most recent years because of its advanced exhibitions in computer vision applications. They have accomplished tremendous growth in the areas of object acknowledgment, recognition and division challenges. Our attention is on models being utilized, information pre-handling and readiness and fittingly preparing the subsequent information or picture. The U – Nets are a very powerful CNNs which has accuracy near to humans. We have created and exploited this CNN architecture, U-Net and have done image segmentation for the brain Magnetic Resonance Images (MRI). The aim of our work is to fundamentally concentrate on the pre-processing of the MRI images, perform Skull Stripping using Deep CNN architecture U-Net and to perform image segmentation. Keywords: Convolution Neural Network (CNN), Magnetic Resonance Imaging (MRI), Skull Stripping,