Online ISSN: 2515-8260

Keywords : Skull Stripping


BRAIN MRI ANALYSIS AND SEGMENTATION USING 2D-UNET ARCHITECTURE

Angelin Beulah. S; Kartikay Kaul; Daksh Chauhan; Hepsiba Mabel. V

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 3, Pages 215-231

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