A state-of-the-art technique to perform cloud-based semantic segmentation using deep learning 3D U-Net architecture

Glioma is the most aggressive and dangerous primary brain tumor with a survival time of less than 14 months. Segmentation of tumors is a necessary task in the image processing of the gliomas and is important for its timely diagnosis and starting a treatment. Using 3D U-net architecture to perform se...

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Bibliographic Details
Published in:	BMC bioinformatics Vol. 23; no. 1; pp. 251 - 21
Main Authors:	Shaukat, Zeeshan, Farooq, Qurat ul Ain, Tu, Shanshan, Xiao, Chuangbai, Ali, Saqib
Format:	Journal Article
Language:	English
Published:	London BioMed Central 24.06.2022 Springer Nature B.V BMC
Subjects:	3D U-Net Accuracy Algorithms Bioinformatics Biomedical and Life Sciences Brain Brain cancer Brain Neoplasms - diagnostic imaging Brain Neoplasms - pathology Brain tumor Brain tumors Cloud Computing Computational Biology/Bioinformatics Computer Appl. in Life Sciences Datasets Deep Learning Experiments Glioma Humans Image processing Image Processing, Computer-Assisted - methods Image segmentation Life Sciences Literature reviews Magnetic resonance imaging Magnetic Resonance Imaging - methods Mathematical analysis Medical diagnosis Methods Microarrays Neural networks Optimization Semantic segmentation Semantics Tomography Traumatic brain injury Tumors Deep learning Brain tumor Cloud computing Semantic segmentation 3D U-Net
ISSN:	1471-2105, 1471-2105
Online Access:	Get full text
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