Semantic Segmentation Issue with output size

Hello everyone,

I am trying to do a segmentation of a Brain Tumor MRI dataset, available in BRATS. But, after I ran my code, I got an error.

"Error using trainNetwork (line 140) Invalid training data. The output size (4) of the last layer doesn't match the number of classes (4).

Error in import_data_alternate2 (line 102) cnn = trainNetwork(trainingData,net,options);

Caused by: Error using nnet.internal.cnn.util.TrainNetworkDataValidator/assertCorrectResponseSizeForOutputLayer (line 217) Invalid training data. The output size (4) of the last layer doesn't match the number of classes (4)."

 clear;
clc;
%Image dataset
pxl = dir('C:\Users\Osvaldo\Downloads\BRATS Data\Imagens\Patient 07\GT\*.png')';
img = fullfile('C:\Users\Osvaldo\Downloads\BRATS Data\Imagens\Patient 07\T1c\');
%Vector preallocation
ground_truth = cell(1,numel(pxl));
gt = cell(1,numel(pxl));
training_data = imageDatastore(img);
%Ground truth images
for k = 1:numel(pxl)
        image = imageDatastore(pxl(k).name);
        ground_truth{k} = image;
end
for k = 1:numel(pxl)
        loc = ground_truth{1,k}.Files;
        gt(k) = loc;
end
gt = gt';
classes = ["Edema" "Non-enhancing tumor" "Necrosis" "Enhancing tumor"];
labelIDs = [ ...
    127 127 127; ... % "Edema"
    190 190 190; ... % "Non-enhancing tumor"
    63 63 63;    ... % "Necrosis"
    255 255 255;     % "Enhancing tumor"
    ];
groundtruth = pixelLabelDatastore(gt,classes,labelIDs);
%CNN creation
inputSize = [429 492 3];
imgLayer = imageInputLayer(inputSize);
filterSize = 3;
numFilters = 32;
conv = convolution2dLayer(filterSize,numFilters,'Padding',1);
relu = reluLayer();
poolSize = 2;
maxPoolDownsample2x = maxPooling2dLayer(poolSize,'Stride',2);
downsamplingLayers = [
    conv
    relu
    maxPoolDownsample2x
    conv
    relu
    maxPoolDownsample2x
    conv
    relu
    maxPoolDownsample2x
    ];
filterSize = 4;
transposedConvUpsample2x = transposedConv2dLayer(4,numFilters,'Stride',2,'Cropping',1);
upsamplingLayers = [
    transposedConvUpsample2x
    relu
    transposedConvUpsample2x
    relu
    ];
numClasses = 4;
conv1x1 = convolution2dLayer(1,numClasses);
finalLayers = [
    conv1x1
    softmaxLayer()
    pixelClassificationLayer()
    ];
net = [
    imgLayer    
    downsamplingLayers
    upsamplingLayers
    finalLayers
    ];
%CNN Training
trainingData = pixelLabelImageSource(training_data,groundtruth);
options = trainingOptions('sgdm', ...
                          'InitialLearnRate', 1e-3, ...
                          'MaxEpochs', 100, ...
                          'MiniBatchSize', 64);
cnn = trainNetwork(trainingData,net,options);

Can someone help me?

[Santo]

Classification of brain tumor of brain mri images using cnn in matlab code . Please can you give?

Answer 1

[Arthur Fernandes]

Hi Gabriel,

Apparently there is an difference between the output size and your classes. It is difficult to debug that since matlab doesn't explicit say the size of the output matrix... For your model to work you need to have the output at the same size as the ground truth. However, since you want to do segmentation, a better way to approach this in matlab is to use the function segnetLayers since it will ensure that the output is in accordance to the ground-truth and will automatically define the number of nodes in each layer. But you still have the flexibility to define the architecture of your network.

[Gabriel VH]

You, mister, are a genius! Thank you very much!

[Farrukh nazir]

Yes, i was also receiving error between output size and network size using u-net. However using segnet, netwotk just started training.

Thanks.

Answer 2

[abdulkader helwan]

numClasses = numel(categories(trainDigitData.Labels)); Then use this variable in the fully connected layer:

fullyConnectedLayer(numClasses).

[Tehmina Kakar]

Thank you so much.