MATLAB: Unexpected image size: All images must have the same size.

Deep Learning Toolboxdigital image processing

Hi, I'm having some problems with a bench of chest xray images. I tryed to use the code from the link below, but it did not work.
https://www.mathworks.com/matlabcentral/answers/385472-error-in-image-size
Error using trainNetwork (line 165)
Unexpected image size: All images must have the same size.
Error in chestXray1 (line 49)
net = trainNetwork(imdsTrain,layers,options);
inputSize = [224 224 1];
numClasses = 2;
layers = [
    imageInputLayer(inputSize)
    convolution2dLayer(5,20)
    batchNormalizationLayer
    reluLayer
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer];
options = trainingOptions('sgdm', ...
    'MaxEpochs',3, ...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',30, ...
    'Verbose',false, ...
    'Plots','training-progress');
net = trainNetwork(imdsTrain,layers,options);

Best Answer

You can use augmentedImageDataStore to resize all images to same size.
Use the following code for your problem:
dataChest = fullfile('/Users/andrebr4/Documents/MATLAB/chestXray/chest_xray');
imds = imageDatastore(dataChest, ...
    'IncludeSubfolders',true, ...
    'LabelSource','foldernames');
%%  Dividir o conjunto de dados em cada categoria
numTrainingFiles = 750;
[imdsTrain,imdsValidation] = splitEachLabel(imds,numTrainingFiles,'randomize');
%%%%%%%code for resizing
inputSize=[224 224 1];
imdsTrain=augmentedImageDatastore(inputSize, imdsTrain,'ColorPreprocessing','rgb2gray');
imdsValidation=augmentedImageDatastore(inputSize, imdsValidation,'ColorPreprocessing','rgb2gray');
%%  Configurar a rede neural
inputSize = [224 224 1];
numClasses = 2;
layers = [
    imageInputLayer(inputSize)
    convolution2dLayer(5,20)
    batchNormalizationLayer
    reluLayer
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer];
%%  Opções de treino
options = trainingOptions('sgdm', ...
    'MaxEpochs',5, ...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',30, ...
    'Verbose',false, ...
    'Plots','training-progress');
%% Treinar a rede neural
net = trainNetwork(imdsTrain,layers,options);
%%  Executar rede treinada no conjunto de teste
YPred = classify(net,imdsValidation);
YValidation = imdsValidation.Labels;
%% Calcular a precisão
accuracy = sum(YPred == YValidation)/numel(YValidation)
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