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Image segmentation is an application area of computer vision and digital image processing that partitions a digital image into multiple image regions or segments. This process involves the extraction of a set of contours from the input digital image in such a manner that pixels belonging to a region share some common characteristics or computed properties, such as color, texture, or intensity. The application domain of image segmentation is widespread, and includes video surveillance, object detection, traffic control systems, and medical imaging. The application of image segmentation techniques in the field of medical imaging can be further subcategorized into virtual surgery simulation, diagnosis, study of anatomical structures, measurement of tissue volumes, location of tumors and other pathologies. In this study, we have proposed two new Convolutional Neural Network (CNN)-based models: (a) S-Net and (b) Attention S-Net (SA-Net) to perform image segmentation tasks in the field of medical imaging, especially to generate segmentation masks for brain tumours if present in brain Medical Resonance Imaging (MRI). Both proposed models were developed by considering U-Net as the base architecture. The newly proposed models have leveraged the concept of ‘Merge Block’ to infuse both the local and global context, and ‘Attention Block’ to focus on the region of interest having a specific object. Additionally, it uses techniques, such as data augmentation to utilize the available annotated samples more efficiently. The proposed models achieved a Dice Similarity Coefficient (DSC) measure of 0.78 and 0.80 for the High-Grade Glioma (HGG) and Low-Grade Glioma (LGG) datasets, respectively.