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matlab-based binary svm classifiers  (MathWorks Inc)


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    MathWorks Inc matlab-based binary svm classifiers
    Matlab Based Binary Svm Classifiers, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/svm+based+classifier/pm32283387-184-2-1
    Average 90 stars, based on 1 article reviews
    matlab-based binary svm classifiers - by Bioz Stars, 2026-09
    90/100 stars

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    Summary of studies applying radiomics for renal tumor differentiation.

    Journal: Cancers

    Article Title: Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

    doi: 10.3390/cancers12061387

    Figure Lengend Snippet: Summary of studies applying radiomics for renal tumor differentiation.

    Article Snippet: Kocak et al. 2018 [ ] , , Three-phasic CECT , 68 RCC patients , , , , , 5.9 (3.3–8.1) , 5.9 (2.0–12.3) , ANN and ML-based SVM classifiers , 275 texture features extracted: , Distinguishing the three main RCC subtypes.

    Techniques: Imaging, Comparison, Biomarker Discovery, Extraction, Diagnostic Assay, Mutagenesis, Selection

    Summary of studies applying radiomics to predict nuclear grade in ccRCC.

    Journal: Cancers

    Article Title: Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

    doi: 10.3390/cancers12061387

    Figure Lengend Snippet: Summary of studies applying radiomics to predict nuclear grade in ccRCC.

    Article Snippet: Kocak et al. 2018 [ ] , , Three-phasic CECT , 68 RCC patients , , , , , 5.9 (3.3–8.1) , 5.9 (2.0–12.3) , ANN and ML-based SVM classifiers , 275 texture features extracted: , Distinguishing the three main RCC subtypes.

    Techniques: Imaging, Biomarker Discovery, Selection, Filtration, Diffusion-based Assay, Variant Assay, Transformation Assay, Comparison

    Summary of studies applying radiomics and gene expression-based models to predict progression in ccRCC.

    Journal: Cancers

    Article Title: Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

    doi: 10.3390/cancers12061387

    Figure Lengend Snippet: Summary of studies applying radiomics and gene expression-based models to predict progression in ccRCC.

    Article Snippet: Kocak et al. 2018 [ ] , , Three-phasic CECT , 68 RCC patients , , , , , 5.9 (3.3–8.1) , 5.9 (2.0–12.3) , ANN and ML-based SVM classifiers , 275 texture features extracted: , Distinguishing the three main RCC subtypes.

    Techniques: Expressing, Functional Assay, RNA Sequencing, Microarray, Gene Expression, Histopathology, Construct