Brain tumors are commonly the cause of blood clots in the brain. If these clots are found early enough, brain cancer patients may experience much reduced morbidity and death. Therefore, correct tumor tissue MRI segmentation is essential for a thorough brain tumor diagnosis and treatment plan. In order to accurately map out confines and obtain extremely accurate segmentation, a number of DL algorithms have been developed for the segmentation of MRIs of brain tumors. We need precise and prompt brain tumor segmentation for treatment and to track the progression of the illness. Hence, this research used a comparative analysis of U-Net and V-Net models for segmentation. Finally, the two models were compared and assessed using the dice coefficient. Based on the comparison, it can be inferred that the U-Net model yields a more precise outcome, as shown by the lowest value of 0.89.