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how to fix this issue through running the program?

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i am using Matlab for medical image classification and i get this issue:
note: i used pre-trained network (alexnet) with .dicom files dataset.
first i prepare my design network
second, i run my code.
>> deepNetworkDesigner
>> SHIVANCLASSIFY
net =
SeriesNetwork with properties:
Layers: [25×1 nnet.cnn.layer.Layer]
InputNames: {'data'}
OutputNames: {'output'}
Error using trainNetwork (line 170)
The training images are of size 227x227x1 but the input layer expects images of size 227x227x3.
Error in SHIVANCLASSIFY (line 36)
net = trainNetwork(augimdsTrain,layers_1,options)
net=alexnet
imds = imageDatastore('lung dataset-Labeled', ...
'IncludeSubfolders',true, 'LabelSource','foldernames', ... % this for labeling by folder names
'FileExtensions','.dcm','ReadFcn',@readDicomDatastoreImage); % this a function
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7);
augmenter = imageDataAugmenter( ...
'RandRotation',[-20,20], ...
'RandXReflection',1,...
'RandYReflection',1,...
'RandXTranslation',[-3 3], ...
'RandYTranslation',[-3 3]);
%augimdsTrain = augmentedImageDatastore([224 224],imdsTrain,'DataAugmentation',augmenter);
%augimdsValidation = augmentedImageDatastore([224 224],imdsValidation,'DataAugmentation',augmenter);
augimdsTrain = augmentedImageDatastore([227 227],imdsTrain);
augimdsValidation = augmentedImageDatastore([227 227],imdsValidation);
options = trainingOptions('rmsprop', ...
'MiniBatchSize',10, ...
'MaxEpochs',20, ...
'InitialLearnRate',1e-3, ...
'Shuffle','every-epoch', ...
'ValidationData',augimdsValidation, ...
'ValidationFrequency',3, ...
'Verbose',false, ...
'Plots','training-progress');
net = trainNetwork(augimdsTrain,layers_1,options)
[YPred, probs] = classify(net,augimdsValidation);
accuracy = mean(YPred ==imdsValidation.Labels)
figure
cm=confusionchart (imdsValidation.Labels, YPred);

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shivan artosh
shivan artosh on 27 Mar 2020
excuse me sir, i got this :
>> SHIVANCLASSIFY
net =
SeriesNetwork with properties:
Layers: [25×1 nnet.cnn.layer.Layer]
InputNames: {'data'}
OutputNames: {'output'}
Error using trainNetwork (line 170)
Invalid training data. The output size (10) of the last layer does not match the number of classes (66).
Error in SHIVANCLASSIFY (line 36)
net = trainNetwork(augimdsTrain,layers_1,options)

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Accepted Answer

Khalid Babutain
Khalid Babutain on 28 Mar 2020
you cannot use pre-trained network unless you adjust it to your data
1. for alexnet, this pre-trained network takes 227x227x3 because it deals with RGB images
2. and that also applies to the first ConveNet which takes 3 channels because its kernels have 3 channels, in which you also have to update
3. you must update the last three classification layers to classify based on your classes
i also think that you are trying to resize your lung dataset to 227x227 in which you may lose some of its quality
this code should work for you, and if it's not clear i can clarify it for you
clear all; close all; clc;
imds = imageDatastore('lung dataset-Labeled', ...
'IncludeSubfolders',true, 'LabelSource','foldernames', ... % this for labeling by folder names
'FileExtensions','.dcm','ReadFcn',@readDicomDatastoreImage); % this a function
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7);
net = alexnet(); % analyzeNetwork(lgraph)
numClasses = numel(categories(imdsTrain.Labels)); % number of classes = number of folders
imageSize = [227 227]; % you can use here the original dataset size
global GinputSize
GinputSize = imageSize;
lgraph = layerGraph(net.Layers);
lgraph = removeLayers(lgraph, 'fc8');
lgraph = removeLayers(lgraph, 'prob');
lgraph = removeLayers(lgraph, 'output');
% create and add layers
inputLayer = imageInputLayer([imageSize 1], 'Name', net.Layers(1).Name,...
'DataAugmentation', net.Layers(1).DataAugmentation, ...
'Normalization', net.Layers(1).Normalization);
lgraph = replaceLayer(lgraph,net.Layers(1).Name,inputLayer);
newConv1_Weights = net.Layers(2).Weights;
newConv1_Weights = mean(newConv1_Weights(:,:,1:3,:), 3); % taking the mean of kernal channels
newConv1 = convolution2dLayer(net.Layers(2).FilterSize(1), net.Layers(2).NumFilters,...
'Name', net.Layers(2).Name,...
'NumChannels', inputLayer.InputSize(3),...
'Stride', net.Layers(2).Stride,...
'DilationFactor', net.Layers(2).DilationFactor,...
'Padding', net.Layers(2).PaddingSize,...
'Weights', newConv1_Weights,...BiasLearnRateFactor
'Bias', net.Layers(2).Bias,...
'BiasLearnRateFactor', net.Layers(2).BiasLearnRateFactor);
lgraph = replaceLayer(lgraph,net.Layers(2).Name,newConv1);
lgraph = addLayers(lgraph, fullyConnectedLayer(numClasses,'Name', 'fc2'));
lgraph = addLayers(lgraph, softmaxLayer('Name', 'softmax'));
lgraph = addLayers(lgraph, classificationLayer('Name','output'));
lgraph = connectLayers(lgraph, 'drop7', 'fc2');
lgraph = connectLayers(lgraph, 'fc2', 'softmax');
lgraph = connectLayers(lgraph, 'softmax', 'output');
% -------------------------------------------------------------------------
augmenter = imageDataAugmenter( ...
'RandRotation',[-20,20], ...
'RandXReflection',1,...
'RandYReflection',1,...
'RandXTranslation',[-3 3], ...
'RandYTranslation',[-3 3]);
%augimdsTrain = augmentedImageDatastore([224 224],imdsTrain,'DataAugmentation',augmenter);
%augimdsValidation = augmentedImageDatastore([224 224],imdsValidation,'DataAugmentation',augmenter);
augimdsTrain = augmentedImageDatastore(imageSize,imdsTrain);
augimdsValidation = augmentedImageDatastore(imageSize,imdsValidation);
options = trainingOptions('rmsprop', ...
'MiniBatchSize',10, ...
'MaxEpochs',20, ...
'InitialLearnRate',1e-3, ...
'Shuffle','every-epoch', ...
'ValidationData',augimdsValidation, ...
'ValidationFrequency',3, ...
'Verbose',false, ...
'Plots','training-progress');
net = trainNetwork(augimdsTrain,lgraph,options)
[YPred, probs] = classify(net,augimdsValidation);
accuracy = mean(YPred ==imdsValidation.Labels)
figure
cm=confusionchart (imdsValidation.Labels, YPred);
you also need to update the readDicomDatastoreImage function to resize every image you read with specified size:
function I = readDicomDatastoreImage(filename)
onState = warning('off', 'backtrace');
c = onCleanup(@() warning(onState));
I = dicomread(filename);
global GinputSize;
I = imresize(I,GinputSize(1:2));

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shivan artosh
shivan artosh on 29 Mar 2020
hello sir
could you please tell me what is that mechanism you have done form me in coding? i mean what is this model name ?
shivan artosh
shivan artosh on 3 Apr 2020
so, what do you think of it?
i use this for classification on medical imgase dataset and obtain a good accuracy.
how can i substituted with CNN ? i want to use CNN model. could you please help me?

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