mlsanityData to 14 Oct 2026

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

arXiv 2210.13336 ↗First posted 24 Oct 2022eess.IV cs.CV cs.LGcited by 9

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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).

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

13 of 33 references.

References · 13

  1. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  2. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
  3. 2015Brain Tumor Segmentation with Deep Neural Networkscited by 137
  4. 2017Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networkscited by 42
  5. 2021Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetcited by 19
  6. 2021The Prominence of Artificial Intelligence in COVID-19cited by 7
  7. 20203D Semantic Segmentation of Brain Tumor for Overall Survival Predictioncited by 6
  8. 2020Context Aware 3D UNet for Brain Tumor Segmentationcited by 4
  9. 2021Brain Tumor Segmentation and Survival Prediction using Automatic Hard mining in 3D CNN Architecturecited by 4
  10. 2021SPI-GAN: Towards Single-Pixel Imaging through Generative Adversarial Networkcited by 3
  11. 2022A Novel Mask R-CNN Model to Segment Heterogeneous Brain Tumors through Image Subtractioncited by 2
  12. 2021RL-NCS: Reinforcement learning based data-driven approach for nonuniform compressed sensingcited by 1
1 more
  1. 2021Fine-Grained Image Generation from Bangla Text Description using Attentional Generative Adversarial Networkcited by 1

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Cited by year

Citing papers per year: 2023: 6; 2024: 2; 2025: 1.

9 papers.

  1. 2023Generative Adversarial Networks for Data Augmentationcited by 7
  2. 2023Introduction to Medical Imaging Informaticscited by 2
  3. 2023Case Studies on X-Ray Imaging, MRI and Nuclear Imagingcited by 2
  4. 2023AutoML Systems For Medical Imagingcited by 1
  5. 2023Online learning for X-ray, CT or MRIcited by 1
  6. 2025Advancing Brain Tumor Segmentation via Attention-based 3D U-Net Architecture and Digital Image Processingcited by 1
  7. 2023Invariant Scattering Transform for Medical Imagingcited by 0
  8. 2024Present and Future of AI in Renewable Energy Domain : A Comprehensive Surveycited by 0
  9. 2024Securing the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant Attackscited by 0