Multiple sclerosis is an auto immune disease which affects the brain and nervous system. A total of 2.8 million people are estimated to live with Multiple sclerosis worldwide (35.9 per 100,000 population). The pooled incidence rate across 75 reporting countries is 2.1 per 100,000 persons per year, and the mean age of diagnosis is 32 years. Lesions resulting from the disease can be spotted in the patients MRI scans. In this paper a novel Deep learning architecture GAU-U-net is proposed. The model is inspired from the very famous U-Net architecture used for semantic segmentation and widely used in medical image segmentation. The proposed model consists of 3D U-Net after adding a new attention technique inspired by the Global Attention Upsample unit. By using GAU-unet architecture the Dice coefficient increased from 64% to 72% compared to using 3D-Unet.Also, when compared with Unet- attention network the dice coefficient increased from 69% to around 72% with a considerable incline in the number of model parameters in favor of our architecture, which uses 28 M parameters compared to Unet-attention which uses100M parameters.