--- a
+++ b/functions/computeClassPerformanceFineTuneCNN.m
@@ -0,0 +1,35 @@
+function [errorStruct] = computeClassPerformanceFineTuneCNN(imagesCell, Labels, folder, inputSize, netTransfer, fidLogs)
+
+% imdsTest = imageDatastore(folder, 'IncludeSubfolders', true, 'LabelSource', 'foldernames');
+
+im_temp = imagesCell{1};
+imsizeOrig = size(im_temp);
+imArray = zeros(imsizeOrig(1), imsizeOrig(2), imsizeOrig(3), numel(imagesCell));
+for ind_im = 1 : numel(imagesCell)
+    imArray(:,:,:,ind_im) = imagesCell{ind_im};
+end %for ind_im
+
+% imdsTestAugm = augmentedImageDatastore(inputSize(1:2), imdsTest);
+imdsTestAugm = augmentedImageDatastore(inputSize(1:2), imArray, categorical(Labels)');
+
+tic
+%[TestOutput, scores] = classify(netTransfer, imArrayTest);
+[TestOutput, scores] = classify(netTransfer, imdsTestAugm, 'MiniBatchSize', 10);
+
+% scores
+% TestOutput
+% double(imdsTest.Labels)
+% oneHotLabels = onehot(double(imdsTest.Labels));
+% correlationProc = computeCorrelation(scores, oneHotLabels)
+
+%cast
+%TestOutput = double(TestOutput)-1;
+timeClass = toc;
+fprintf_pers(fidLogs, ['\t\tTime for classification: ' num2str(timeClass) ' s\n']);
+%Confusion matrix
+%C_knn = confusionmat(TestLabels, TestOutput);
+C_knn = confusionmat(categorical(Labels), TestOutput);
+
+%Error metrics
+errorStruct = computeErrorsFromCM(C_knn);
+