Correction: Staining Pattern Classification of Antinuclear Autoantibodies Based on Block Segmentation in Indirect Immunofluorescence Images

Compared with the original results, the classification accuracy based on whole image decreases from 83.08% to 69.88%, and that based on cell segmentation also decreases from 90.77% to 84.34%. [...]the classification accuracy of these block images also reduces slightly, from 79.95% to 74.63% using local binary pattern (LBP) and Back Propagation Neural Network (BPNN), and from 82.21% to 78.72% using LBP and K-nearest neighbor (KNN). [...]with the reduction of database size, the classification results based on other methods, such as grey-level co-occurrence matrix (GLCM), linear discrimination analysis (LDA) and scale-invariant feature transform (SIFT), decrease. The following codes were included: * PCA, which is available at http://www.cad.zju.edu.cn/home/dengcai/Data/DimensionReduction.html * GLCM_Feature1.m, which is available at https://www.mathworks.com/matlabcentral/fileexchange/22187-glcm-texture-features * Vlfeat, which is available at http://www.vlfeat.org/ * HOG [3], which is available at https://www.mathworks.com/matlabcentral/fileexchange/28689-hog-descriptor-for-matlab * getAllFiles.m, which is available at https://stackoverflow.com/revisions/2654459/3 * create_pr_net.m, which is available at https://github.com/ankitkala/Pattern-Recognition/blob/master/impact/create_pr_net.m In light of concerns that the original license requirements were not met and/or permission to publish was not obtained for these codes, the original article [1] has been republished on July 10, 2020, with a revised version of Supporting Information files, in which the re-used code is replaced by the relevant links.