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

Autor: Zeeshan Shaukat, Qurat ul Ain Farooq, Shanshan Tu, Chuangbai Xiao, Saqib Ali
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: BMC Bioinformatics, Vol 23, Iss 1, Pp 1-21 (2022)
Druh dokumentu: article
ISSN: 1471-2105
DOI: 10.1186/s12859-022-04794-9
Popis: Abstract 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 semantic segmentation on brain tumor dataset is at the core of deep learning. In this paper, we present a unique cloud-based 3D U-Net method to perform brain tumor segmentation using BRATS dataset. The system was effectively trained by using Adam optimization solver by utilizing multiple hyper parameters. We got an average dice score of 95% which makes our method the first cloud-based method to achieve maximum accuracy. The dice score is calculated by using Sørensen-Dice similarity coefficient. We also performed an extensive literature review of the brain tumor segmentation methods implemented in the last five years to get a state-of-the-art picture of well-known methodologies with a higher dice score. In comparison to the already implemented architectures, our method ranks on top in terms of accuracy in using a cloud-based 3D U-Net framework for glioma segmentation.
Databáze: Directory of Open Access Journals
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