Optimum feature set for classification using both core and margin information utilizing GLCM, GRLM, and GLSZM texture methods and SVM-RBF classification algorithm.
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下载链接：
https://figshare.com/articles/dataset/Optimum_feature_set_for_classification_using_both_core_and_margin_information_utilizing_GLCM_GRLM_and_GLSZM_texture_methods_and_SVM-RBF_classification_algorithm_/13512229
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资源简介：
A maximum of 10 features was selected for classification. Model performance was evaluated using LOOCV method. Features were selected using forward SFS based on F1-score metric. Textural features, for example Core-MBF-CON: GLCM contrast parameter of MBF parametric image from core ROI and Margin-MBF-SALGE: GLSZM small area low gray level emphasis parameter of MBF parametric image from margin ROI, were the dominant features that contributed to hybrid biomarkers that best separated the two lesion types.
创建时间：
2020-12-31
