Cancer of the brain is deadly and requires careful surgical segmentation. The brain tumors were segmented using U-Net using a Convolutional Neural Network (CNN). When looking for overlaps of necrotic, edematous, growing, and healthy tissue, it might be hard to get relevant information from the images. The 2D U-Net network was improved and trained with the BraTS datasets to find these four areas. U-Net can set up many encoder and decoder routes that can be used to get information from images that can be used in different ways. To reduce computational time, we use image segmentation to exclude insignificant background details. Experiments on the BraTS datasets show that our proposed model for segmenting brain tumors from MRI (MRI) works well. In this study, we demonstrate that the BraTS datasets for 2017, 2018, 2019, and 2020 do not significantly differ from the BraTS 2019 dataset's attained dice scores of 0.8717 (necrotic), 0.9506 (edema), and 0.9427 (enhancing).
@article{arxiv.2210.13336,
title = {Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis},
author = {MD Abdullah Al Nasim and Abdullah Al Munem and Maksuda Islam and Md Aminul Haque Palash and MD. Mahim Anjum Haque and Faisal Muhammad Shah},
journal= {arXiv preprint arXiv:2210.13336},
year = {2023}
}