Warning: Escaped character '\I' is not valid. See 'doc sprintf' for supported special characters. MatlabR2019a

Hi everybody, I have a big problem with export_fig, is giving me this error:

Warning: Escaped character '\I' is not valid. See 'doc sprintf' for supported special characters. 
In export_fig>parse_args (line 1543)
In export_fig (line 354)
In preprocessMI (line 118) 
Error using export_fig>parse_args (line 1543)
Folder C:\Users\busta\Downloads\gwbz3fsgp8-1\covid19_ECG\preprocessed_dataset\normal
Error in export_fig (line 354)
    [fig, options] = parse_args(nargout, fig, argNames, varargin{:});
Error in preprocessMI (line 118)
         export_fig(kayit_yeri ,'-transparent', '-r300')%-m2.5

The code I try to run is this:

clear all;
close all;
Base  = 'C:\\Users\\busta\\Downloads\\gwbz3fsgp8-1\\ECG Images of Patient that have History of MI (203)';
List  = dir(fullfile(Base, '**', '*.jpg'));
Files = fullfile({List.folder}, {List.name});
load('coordinates.mat');
mi_statistical=[];
iFile = 1

 figure, set(gcf,'visible','off');
for iFile = 1:numel(Files)

    I = imread(Files{iFile});
    %imshow(I)
    %k?rpma

     [J,rect] = imcrop(I); %koordinatlar? bulmak icin
    %[103.5 104.5 723 432] t?m ecg
    I2 = imcrop(I,[71.5 287.5 2102 1228]);  
    %imshow(I2)
    [J,rect] = imcrop(I2); %koordinatlar? bulmak icin
    I3 = imcrop(I2,[40.5 4.5 152 90]); % bir kanal
    %imshow(I_image)

    I_image= imcrop(I2,[120.5 0.5 315 315]);
    aVR_image= imcrop(I2,[672.5 0.5 315 315]); 
    V1_image=imcrop(I2,[1133.5 0.5 315 315]); 
    V4_image= imcrop(I2,[1639.5 0.5 315 315]); 
    II_image= imcrop(I2,[120.5  315.5 315 315]); 
    aVL_image= imcrop(I2,[672.5 315.5 315 315]);
    V2_image=imcrop(I2,[1133.5 315.5 315 315]);
    V5_image= imcrop(I2,[1639.5 315.5 315 315]);
    III_image= imcrop(I2,[120.5 630.5 315 315]);
    aVF_image= imcrop(I2,[672.5 630.5 315 315]);
    V3_image=imcrop(I2,[1133.5 630.5 315 315]);
    V6_image= imcrop(I2,[1639.5 630.5 315 315]);

    % -li sinyal olu?turma
    I_image_neg=I_image(end:-1:1,:,:); figure, imshow(I_image_neg)
    aVR_image_neg=aVR_image(end:-1:1,:,:);
    II_image_neg=II_image(end:-1:1,:,:);
    aVL_image_neg=aVL_image(end:-1:1,:,:);
    III_image_neg=III_image(end:-1:1,:,:);
    aVF_image_neg=aVF_image(end:-1:1,:,:);

    all_cropped_image=cat(4, I_image, aVL_image, III_image_neg, aVF_image_neg, ...
        II_image_neg, aVR_image, I_image_neg, aVL_image_neg, III_image, aVF_image,...
        II_image, aVR_image_neg, V1_image, V2_image, V3_image, V4_image, V5_image, V6_image );
    size(all_cropped_image)
    %figure, imshow(all_cropped_image(:,:,:,5));

    coordinate_labels=["I", "aVL" , "III(-)" ,"aVF(-)" ,"II(-)" ,"aVR", "I(-)" , "aVL(-)", "III", "aVF", "II", "aVR(-)", "V1", "V2", "V3", "V4", "V5", "V6"];

    %adjust
    for i=1:18

        K = imadjust(all_cropped_image(:,:,:,i),[0.1 0.7],[]);
        figure
        %imshow(K)
        % arkaplan kald?rma

        binaryImage = K(:, :, 2) < 250; % Or whatever threshold works.
        binaryImage = bwareafilt(binaryImage, 1); % Extract only the largest blob.
        figure, imshow(1-binaryImage)
        all_cropped_image_binary(:,:,i)=(binaryImage);

%         
         %figure, imshow (all_cropped_image_binary(:,:,5))
         switch (i)
             case 1
                 channel='\I\';
             case 11
                 channel='\II\';
             case 9
                 channel='\III\';
             case 6
                 channel='\aVR\';
             case 2
                 channel='\avL\';
             case 10
                 channel='\avF\';
             case 13
                 channel='\V1\';
             case 14
                 channel='\V2\';
             case 15
                 channel='\V3\';
             case 16
                 channel='\V4\';
             case 17
                 channel='\V5\';
             case 18
                 channel='\V6\';
             case 7
                 channel='\I(-)\';
             case 5
                 channel='\II(-)\';
             case 3
                 channel='\III(-)\';
             case 12
                 channel='\aVR(-)\';
             case 8
                 channel='\avL(-)\';
             case 4
                 channel='\avF(-)\';

         end
%         
          coordinate_labels=["I", "aVL" , "III(-)" ,"aVF(-)" ,"II(-)" ,"aVR", "I(-)" , "aVL(-)", "III", "aVF", "II", "aVR(-)", "V1", "V2", "V3", "V4", "V5", "V6"];
%         
%         %%save ECG images
        kayit_yeri=strcat(sprintf('%s%d','C:\\Users\\busta\\Downloads\\gwbz3fsgp8-1\\covid19_ECG\\preprocessed_dataset\\normal'),channel);
         kayit_yeri=strcat(kayit_yeri,num2str(iFile));
         kayit_yeri=strcat(kayit_yeri,'.png');
         %imshow (1-all_cropped_image_binary(:,:,i))
         export_fig(kayit_yeri ,'-transparent', '-r300')%-m2.5
%         
%         
    end % 12 channel

    comatrix
    comat=[];
    for k=1:18
       comat= [comat graycomatrix(logical(all_cropped_image_binary(:,:,k)))];

    end

    %as?l feature cikarma burasi, eskiden matrix al?yorduk ?imdi burada her?eyi
    %d?zg?nce hesapl?yoruz
    comat_energy=[];
    comat_correlation=[];
    comat_contrast=[];
    comat_homogeneity=[];

    for k=1:18
        glcms=graycomatrix(logical(all_cropped_image_binary(:,:,k)));
        stats = graycoprops(glcms);% Calculate properties of gray-level co-occurrence matrix

        comat_energy= [comat_energy stats.Energy];
        comat_correlation= [comat_correlation stats.Correlation];       
        comat_contrast= [comat_contrast stats.Contrast];
        comat_homogeneity= [comat_homogeneity stats.Homogeneity];
%         
    end %feature exraction

    %statistical difference
    mi_statistical= [mi_statistical; comat_energy'];
    statistical_label= ["Energy","Correlation","Contrast","Homogeneity"] ;

    %loksayona g?re haritalama

     x_coordinates = [2.5; 1.5; 3; 2; 1; 2; 3; 5; 6; 7; 6; 5; 6.5; 5.5];
     y_coordinates = [7; 5.7; 5.8; 5; 4; 2; 1; 1; 2; 4; 5; 5.8; 6; 7];
     xi=linspace(min(x_coordinates),max(x_coordinates),100);
     yi=linspace(min(y_coordinates),max(y_coordinates),100);
     [XI YI]=meshgrid(xi,yi);
     ZI = griddata(x_coordinates,y_coordinates,comat_energy(1,1:end)',XI,YI,'natural');
%     
     figure, set(gcf,'visible','off');
     contourf(XI,YI,ZI,50,'edgecolor','none');
     colormap(hsv);
     axis off
     set(gcf,'position',[-15,15,710,720])
     set(gca,'LooseInset',get(gca,'TightInset'))

%     %save features map
     kayit_yeri=strcat('C:\\Users\\busta\\Downloads\\gwbz3fsgp8-1\\covid19_ECG\\feature_maps\\normal',num2str(iFile));
     kayit_yeri=strcat(kayit_yeri,'.png');
     export_fig(kayit_yeri ,'-transparent')%-m2.5

end% dosya

It's from a project of Deep Learning, but I can't run this or other similar files. Can you help me? I'm using Windows 10 21H1 and Matlab R2019a

altmany / export_fig

Warning: Escaped character '\I' is not valid. See 'doc sprintf' for supported special characters. MatlabR2019a #345