WU-Net++: A novel enhanced Weighted U-Net++ model for brain tumor detection and segmentation from multi-parametric magnetic resonance scans-Reference-Cited by-同舟云学术

WU-Net++: A novel enhanced Weighted U-Net++ model for brain tumor detection and segmentation from multi-parametric magnetic resonance scans

Published:2024-02-08 Issue:28 Volume:83 Page:71885-71908
ISSN:1573-7721
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Das Suchismita,Dubey Rajni,Jena Biswajit,Tsai Lung-Wen,Saxena Sanjay^ORCID

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11042-024-18336-3.pdf

Reference84 articles.

1. Jena B, Saxena S, Nayak GK, Balestrieri A, Gupta N, Khanna NN, ... Suri JS (2022) Brain tumor characterization using radiogenomics in artificial intelligence framework. Cancers 14(16):4052. https://doi.org/10.3390/cancers14164052

2. Jena B, Nayak GK, Saxena S (2022) An empirical study of different machine learning techniques for brain tumor classification and subsequent segmentation using hybrid texture feature. Mach Vis Appl 33(1):6. https://doi.org/10.1007/s00138-021-01262-x

3. Rouse C, Gittleman H, Ostrom QT, Kruchko C, Barnholtz-Sloan JS (2015) Years of potential life lost for brain and CNS tumors relative to other cancers in adults in the United States, 2010. Neuro Oncol 18(1):70–77. https://doi.org/10.1093/neuonc/nov249

4. Das S, Nayak GK, Saba L, Kalra M, Suri JS, Saxena S (2022) An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review. Comput Biol Med 143:105273. https://doi.org/10.1016/j.compbiomed.2022.105273

5. Farmanfarma KK, Mohammadian M, Shahabinia Z, Hassanipour S, Salehiniya H (2019) Brain cancer in the world: an epidemiological review. World Cancer Res J 6(5):1–5