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In the quantitative assessment of brain tumors, tumor identification is a serious challenge. Magnetic Resonance Imaging (MRI) has grown in popularity in recent years as a result of its non-invasive and powerful soft tissue comparison capabilities. The practice of using magnetic resonance imaging (MRI) to find brain tumors is quite prevalent. The MRI generates a massive quantity of data. Because of tumor heterogeneity features, manual segmentation takes a long time, limiting the use of accurate quantitative measurements in clinical practice. Manual segmentation is a time-consuming procedure in clinical practice, and its success is greatly dependent on the operator's expertise. Accurate and automated tumor segmentation methods are also required; however, automatic segmentation of brain tumors is challenging due to their significant spatial and structural heterogeneity. This research suggests employing encoder-decoder based convolutional neural networks to completely automate the segmentation of brain tumors. The paper focuses on UNet which is a semantic segmentation deep neural networks for segmenting tumors from brain MRI images. The networks are trained and evaluated on a publicly available standard dataset, with the Dice Similarity Coefficient (DSC) serving as the measure for the whole projected picture, including tumor and backdrop.