Tumor segmentation from brain MRI sequences is usually done manually by the radiologist. Being a highly tedious and error prone task, mainly due to factors such as human fatigue, overabundance of MRI slices per patient, and increasing number of patients, manual operations often lead to inaccurate delineation. Moreover, use of qualitative measures of evaluation by radiologists results in high inter- and intraobserver error rates. There is an evident need for automated systems to perform this task. To this end Pereira et al. propose to use a deep learning method called Convolutional Neural Network for predicting a segmentation mask of the patient MRI scans. The approach the segmentation problem as a pixel-wise classification problem, where each pixel in the input MRI scan 2D slice is classified into one of the five categories: background, necrosis, edema, non-enhancing and enhancing region. The authors propose two networks, one for High Grade Gliomas (HGG) and one for Low Grade Gliomas (LGG). The HGG network has more number of convolution layers than the LGG due to lack of data. The proposed network architectures are a combination of convolution, relu, and max pooling layers, followed by some fully connected layers in the end. The networks are trained using categorical cross-entropy loss function. The networks were trained on 2D 33x33 patches extracted from the 2D MRI slices of the brain from the BRATS 2012 dataset. The patches were randomly sampled from the images and the task was to predict the class of the pixel in the middle of the patch. To approach the problem of class imbalance, they sample approximately 40\% of the patches were normal patches. They also use data augmentation to increase the number of effective patches to train. In order to test the performance of their proposed method, as well as understand the impact of various hyperparameters, the authors perform a multitude of tests. The tests included turning data augmentation on/off, using leaky relu instead of relu, changing patch extraction plane, using deeper networks, and using larger kernels.