# Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis

arXiv: https://arxiv.org/abs/2210.13336 · HTML: https://mlsanity.com/paper/2210.13336/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 13 of 13)

- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Brain Tumor Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1505.03540/) (2015)
- [Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks](https://mlsanity.com/paper/1705.03820/) (2017)
- [Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet](https://mlsanity.com/paper/2104.00985/) (2021)
- [The Prominence of Artificial Intelligence in COVID-19](https://mlsanity.com/paper/2111.09537/) (2021)
- [3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction](https://mlsanity.com/paper/2008.11576/) (2020)
- [Context Aware 3D UNet for Brain Tumor Segmentation](https://mlsanity.com/paper/2010.13082/) (2020)
- [Brain Tumor Segmentation and Survival Prediction using Automatic Hard mining in 3D CNN Architecture](https://mlsanity.com/paper/2101.01546/) (2021)
- [SPI-GAN: Towards Single-Pixel Imaging through Generative Adversarial Network](https://mlsanity.com/paper/2107.01330/) (2021)
- [A Novel Mask R-CNN Model to Segment Heterogeneous Brain Tumors through Image Subtraction](https://mlsanity.com/paper/2204.01201/) (2022)
- [RL-NCS: Reinforcement learning based data-driven approach for nonuniform compressed sensing](https://mlsanity.com/paper/2107.00838/) (2021)
- [Fine-Grained Image Generation from Bangla Text Description using Attentional Generative Adversarial Network](https://mlsanity.com/paper/2109.11749/) (2021)

## Built on by (most cited first, 9 of 9)

- [Generative Adversarial Networks for Data Augmentation](https://mlsanity.com/paper/2306.02019/) (2023)
- [Introduction to Medical Imaging Informatics](https://mlsanity.com/paper/2306.00421/) (2023)
- [Case Studies on X-Ray Imaging, MRI and Nuclear Imaging](https://mlsanity.com/paper/2306.02055/) (2023)
- [AutoML Systems For Medical Imaging](https://mlsanity.com/paper/2306.04750/) (2023)
- [Online learning for X-ray, CT or MRI](https://mlsanity.com/paper/2306.06491/) (2023)
- [Advancing Brain Tumor Segmentation via Attention-based 3D U-Net Architecture and Digital Image Processing](https://mlsanity.com/paper/2510.19109/) (2025)
- [Invariant Scattering Transform for Medical Imaging](https://mlsanity.com/paper/2307.04771/) (2023)
- [Present and Future of AI in Renewable Energy Domain : A Comprehensive Survey](https://mlsanity.com/paper/2406.16965/) (2024)
- [Securing the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant Attacks](https://mlsanity.com/paper/2408.00348/) (2024)

## Abstract (arXiv)

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).