The task of detecting tumors is an important one as it can lead to many lives being saved as nearly 20k lives are lost every year due to brain tumors. The task is challenging as in few cases brain tumors are not detected in the early stages. With the help of artificial intelligence, this gap can be bridged. The task of detecting regions of tumors can be classified into the popular computer vision task of image segmentation. Given the actual MRI scans and the masks representing the region of the tumor, the machine learning models can learn the functions which map the regions to the presence or nonpresence of the tumor. The models are trained using a pair of MRI images and corresponding masks and the given an MRI image, the model should detect the region of the tumor if present. This paper explores the different state-of-the-art architectures to perform the task of image segmentation.