Brain tumor segmentation is one of the most important and difficult tasks in the field of medical image processing as a human-assisted manual categorization can result in inaccurate prediction and diagnosis. Furthermore, it is a difficult process when there is a huge amount of data to assist. Because brain tumors have such a wide range of appearances and because tumor and normal tissues are so comparable, extracting tumor regions from images becomes difficult. In this paper, U-Net was used for the detection and segmentation of brain tumors from MRI images. The applied model was evaluated on real images provided by Medical Image Computing and Computer-Assisted Interventions BRATS 2020 datasets. Test accuracy of 99.4% has been achieved using the above-mentioned dataset.
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View Code? Open in Web Editor NEWBrain tumor segmentation is one of the most important and difficult tasks in the field of medical image processing as a human-assisted manual categorization can result in inaccurate prediction and diagnosis. Furthermore, it is a difficult process when there is a huge amount of data to assist. Because brain tumors have such a wide range of appearances and because tumor and normal tissues are so comparable, extracting tumor regions from images becomes difficult. In this paper, U-Net was used for the detection and segmentation of brain tumors from MRI images. The applied model was evaluated on real images provided by Medical Image Computing and Computer-Assisted Interventions BRATS 2020 datasets. Test accuracy of 99.4% has been achieved using the above-mentioned dataset.