Brain Tumor detection based on MRI Image Segmentation Using U-Net

Main Article Content

S. Raghu, T. Adi Lakshmi


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.

Article Details

How to Cite
S. Raghu, T. Adi Lakshmi. (2022). Brain Tumor detection based on MRI Image Segmentation Using U-Net. Annals of the Romanian Society for Cell Biology, 26(01), 579–594. Retrieved from