Writing TIFFs with 9 components is not supported with IMWRITE. Use Tiff instead. Type "help Tiff" for more information.

clear;clc;close all


% Load the Image Dataset of Normal and Malignant WBC
imdsTrain = imageDatastore('D:\Project\DB1\train','IncludeSubfolders',true,'LabelSource','foldernames');
imdsTest = imageDatastore('D:\Project\DB1\test','IncludeSubfolders',true,'LabelSource','foldernames');
%Perform Cross-Validation using Hold-out method with a percentage split of 70% training and 30% testing
%[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');

%% 
%%
newext = '.tif';

while hasdata(imdsTrain)
    [img, info] = read(imdsTrain);
    [filedir, basename, ext] = fileparts(info.Filename);
    newfilename = fullfile(filedir, [basename, newext]);
    img3 = repmat( imresize(im2uint8(img), [299 299]), [1 1 3] );
    imwrite(img3, newfilename);
end

while hasdata(imdsTest)
    [img, info] = read(imdsTest);
    [filedir, basename, ext] = fileparts(info.Filename);
    newfilename1 = fullfile(filedir, [basename, newext]);
    img3 = repmat( imresize(im2uint8(img), [299 299]), [1 1 3] );
    imwrite(img3, newfilename1);
end
load('HW');
%%

%Select the Test images and save in Y_test
Y_test = newfilename1.UnderlyingDatastore.Labels;
%%

% optimzation techniques selection and hyperparamter selection
options = trainingOptions('adam', ...
    'MiniBatchSize',16, ...
    'MaxEpochs',20, ...
    'InitialLearnRate',1e-4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',newfilename1, ...
    'ValidationFrequency',3, ...
    'Verbose',false, ...
    'Plots','training-progress');
%%
%CNN model training
netTransfer = trainNetwork(newfilename,HW,options);

%%

% for i=1:numel(imdsValidation.Files)
%      a=[imdsValidation.Files(i)];
%      a = imread(char(a));
% %     featuresTest22 = activations(net,a,layer,'OutputAs','rows');
%     YPred(i) = classify(netTransfer,a);
%     imshow(a),title(char(YPred));
% i
% end
%%
% CNN Model validation
YPred = classify(netTransfer,newfilename1);

%Performance evaluation of Deep Learning Trained Model
plotconfusion(Y_test,YPred)

Error using writetif (line 40)

Writing TIFFs with 9 components is not supported with IMWRITE. Use Tiff instead. Type "help Tiff" for

more information.

Error in imwrite (line 546)

feval(fmt_s.write, data, map, filename, paramPairs{:});

Error in CNN1 (line 19)

imwrite(img3, newfilename);

[Walter Roberson]

https://www.mathworks.com/matlabcentral/answers/1627970-1-bit-tiff-images-having-more-than-1-channel-are-not-supported#comment_1944450

Answer 1

[yanqi liu]

clear;clc;close all


% Load the Image Dataset of Normal and Malignant WBC
imdsTrain = imageDatastore('D:\Project\DB1\train','IncludeSubfolders',true,'LabelSource','foldernames');
imdsTest = imageDatastore('D:\Project\DB1\test','IncludeSubfolders',true,'LabelSource','foldernames');
%Perform Cross-Validation using Hold-out method with a percentage split of 70% training and 30% testing
%[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');

%%
%%
newext = '.tif';

while hasdata(imdsTrain)
    [img, info] = read(imdsTrain);
    [filedir, basename, ext] = fileparts(info.Filename);
    newfilename = fullfile(filedir, [basename, newext]);
    if ndims(img) == 2
        img3 = repmat( imresize(im2uint8(img), [299 299]), [1 1 3] );
    else
        img3 =  imresize(im2uint8(img), [299 299]);
    end
    imwrite(img3, newfilename);
end

while hasdata(imdsTest)
    [img, info] = read(imdsTest);
    [filedir, basename, ext] = fileparts(info.Filename);
    newfilename1 = fullfile(filedir, [basename, newext]);
    if ndims(img) == 2
        img3 = repmat( imresize(im2uint8(img), [299 299]), [1 1 3] );
    else
        img3 =  imresize(im2uint8(img), [299 299]);
    end
    imwrite(img3, newfilename1);
end
load('HW');
%%

%Select the Test images and save in Y_test
Y_test = newfilename1.UnderlyingDatastore.Labels;
%%

% optimzation techniques selection and hyperparamter selection
options = trainingOptions('adam', ...
    'MiniBatchSize',16, ...
    'MaxEpochs',20, ...
    'InitialLearnRate',1e-4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',newfilename1, ...
    'ValidationFrequency',3, ...
    'Verbose',false, ...
    'Plots','training-progress');
%%
%CNN model training
netTransfer = trainNetwork(newfilename,HW,options);

%%

% for i=1:numel(imdsValidation.Files)
%      a=[imdsValidation.Files(i)];
%      a = imread(char(a));
% %     featuresTest22 = activations(net,a,layer,'OutputAs','rows');
%     YPred(i) = classify(netTransfer,a);
%     imshow(a),title(char(YPred));
% i
% end
%%
% CNN Model validation
YPred = classify(netTransfer,newfilename1);

%Performance evaluation of Deep Learning Trained Model
plotconfusion(Y_test,YPred)

[sun rise]

Dot indexing is not supported for variables of this type.

Error in CNN1 (line 40)

Y_test = newfilename1.UnderlyingDatastore.Labels;

[yanqi liu]

yes，sir，newfilename1 now is just string，use for filename

so，if use it as datastore，may be use imageDatastore again to get image object

if possible，may be upload your image file zip to do some analysis

[sun rise]

My DB.. Thank you in advance