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Evaluation of magnetic resonance image segmentation in brain low-grade gliomas using support vector machine and convolutional neural network

Quantitative Imaging in Medicine and Surgery(2020)10.21037/qims-20-783Source: DataRank Database

Evaluation of magnetic resonance image segmentation in brain low-grade gliomas using support vector machine and convolutional neural network is a research paper published in Quantitative Imaging in Medicine and Surgery (2020). On theSindex it has a DataRank of 0. It has been cited 35 times.

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Open Access35 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (1 grants)OA: goldTopics (6)Linked Datasets (2)DataCite Reuse (2)IDs (PubMed, PMC)Annotations (1)

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Authors (4)

Huijuan Zhang ORCID,Jun Xia ORCID,Xiaoliang Zhang ORCID,Qifan Yang ORCID

Live enrichment

PubMed:33392030 PMC:PMC7719950

NIBIB NIH HHS

Grant: U01 EB023829

goldLicense: cc-by-nc-nd

publisherhttps://qims.amegroups.com/article/viewFile/51817/pdf

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/7719950

Keywords

Support vector machine (SVM)Image segmentationMagnetic resonance imaging (MRI)Machine LearningConvolutional Neural Network (Cnn)Low-grade Glioma (Lgg)

Additional file 1 of MODILM: towards better complex diseases classification using a novel multi-omics data integration learning model

10.6084/m9.figshare.26587239.v1figshare

Additional file 1 of MODILM: towards better complex diseases classification using a novel multi-omics data integration learning model

10.6084/m9.figshare.26587239figshare

Evaluation of magnetic resonance image segmentation in brain low-grade gliomas using support vector machine and convolutional neural network

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Authors (4)

Funding (1 grants)

Open Access

Topics

Linked Datasets (2)

Annotations (Europe PMC)