# Brain Tumor Segmentation from MRI Images using Deep Learning Techniques

arXiv: https://arxiv.org/abs/2305.00257 · HTML: https://mlsanity.com/paper/2305.00257/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 29 Apr 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [ResUNet++: An Advanced Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1911.07067/) (2019)
- [Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation](https://mlsanity.com/paper/1802.06955/) (2018)
- [U-Net and its variants for medical image segmentation: theory and applications](https://mlsanity.com/paper/2011.01118/) (2020)
- [Optimization for Medical Image Segmentation: Theory and Practice when evaluating with Dice Score or Jaccard Index](https://mlsanity.com/paper/2010.13499/) (2020)
- [Brain Tumor Type Classification via Capsule Networks](https://mlsanity.com/paper/1802.10200/) (2018)
- [DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR Images](https://mlsanity.com/paper/2004.12333/) (2020)
- [A Systematic Approach for MRI Brain Tumor Localization, and Segmentation using Deep Learning and Active Contouring](https://mlsanity.com/paper/2102.03532/) (2021)
- [Deep Medical Image Analysis with Representation Learning and Neuromorphic Computing](https://mlsanity.com/paper/2005.05431/) (2020)

## Built on by (most cited first, 2 of 2)

- [OneNet: A Channel-Wise 1D Convolutional U-Net](https://mlsanity.com/paper/2411.09838/) (2024)
- [Systematic Review of Pituitary Gland and Pituitary Adenoma Automatic Segmentation Techniques in Magnetic Resonance Imaging](https://mlsanity.com/paper/2506.19797/) (2025)

## Abstract (arXiv)

A brain tumor, whether benign or malignant, can potentially be life threatening and requires painstaking efforts in order to identify the type, origin and location, let alone cure one. Manual segmentation by medical specialists can be time-consuming, which calls out for the involvement of technology to hasten the process with high accuracy. For the purpose of medical image segmentation, we inspected and identified the capable deep learning model, which shows consistent results in the dataset used for brain tumor segmentation. In this study, a public MRI imaging dataset contains 3064 TI-weighted images from 233 patients with three variants of brain tumor, viz. meningioma, glioma, and pituitary tumor. The dataset files were converted and preprocessed before indulging into the methodology which employs implementation and training of some well-known image segmentation deep learning models like U-Net & Attention U-Net with various backbones, Deep Residual U-Net, ResUnet++ and Recurrent Residual U-Net. with varying parameters, acquired from our review of the literature related to human brain tumor classification and segmentation. The experimental findings showed that among all the applied approaches, the recurrent residual U-Net which uses Adam optimizer reaches a Mean Intersection Over Union of 0.8665 and outperforms other compared state-of-the-art deep learning models. The visual findings also show the remarkable results of the brain tumor segmentation from MRI scans and demonstrates how useful the algorithm will be for physicians to extract the brain cancers automatically from MRI scans and serve humanity.