The training image are the size of [32 32 32 4] but the input layer expects image size [32 32 32 3]

I train the network the following :

imageDir=fullfile(tempdir,'PP');
if ~exist(imageDir,'dir')
    mkdir(imageDir);
end
sourceDatalaoc=[imageDir filesep 'Brain'];
volc=fullfile (sourceDatalaoc, 'AA')
if~ exist(volc,'dir')
    mkdir(volc);
end
loc=fullfile (sourceDatalaoc, 'BB')
if~ exist(loc,'dir')
    mkdir(loc);
end
 
imds = imageDatastore(volc,'FileExtensions','.nii','ReadFcn',@sampleReadeFcn);
classNames = ["edema","nonEnhancingTumor","enhancingTumour"];
pixelLabelID = [1 2 3];
pxds = pixelLabelDatastore(loc,classNames,pixelLabelID,'FileExtensions','.nii','ReadFcn',@sampleReadeFcn);
V = read(imds);
L = read(pxds);
%h = labelvolshow(L,V(:,:,:,1));
pximds = pixelLabelImageDatastore(imds,pxds);
inputPatchSize = [32 32 32 4];
numClasses = 2;
patchSize = [32 32 32];
patchPerImage = 8;
miniBatchSize = 16;
patchds = randomPatchExtractionDatastore(imds,pxds,patchSize, ...
    'PatchesPerImage',patchPerImage);
patchds.MiniBatchSize = miniBatchSize;
layers = [
         image3dInputLayer([32 32 32 3)
         convolution3dLayer(3,12,'Stride',1,'Padding','Same')
         batchNormalizationLayer
         reluLayer
         transposedConv3dLayer(3,6,'Stride',1,'Cropping',1)
         batchNormalizationLayer
         reluLayer
         convolution3dLayer(1,2)
         softmaxLayer
         pixelClassificationLayer
      ]
opts = trainingOptions('sgdm', ...
    'InitialLearnRate',1e-3, ...
            'ExecutionEnvironment','CPU',...
    'MaxEpochs',100);
 [net2,info] = trainNetwork(patchds,layers,opts);

Error:The training image are the size of [32 32 32 4] but the input layer expects image size [32 32 32 3]

[Walter Roberson]

There is no image input layer that can handle 4d but nifti files can be 4d (with time if I recall correctly)

How do you want to handle the fact that your input has a time dimension?

Answer 1

[hla hla myint]

Thanks Saya ,

Input image is [240 240 155 4] and nii file.

Answer 2

[Walter Roberson]

You are trying to create a deep network using image processing layers, but your data is 4D. None of the available image layers support 4D.

If your 4th dimension represents time, then you should be considering a sequence layer; https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.sequenceinputlayer.html