Question
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);
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Expert Answer
Kshitij Singh
PhD Expert
Answered Aug 14, 2026
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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