how to fix this issue through running the program?
Learn how to fix program issues quickly by running the program! This guide provides step-by-step instructions & troubleshooting tips to resolve errors effective
Learn how to fix program issues quickly by running the program! This guide provides step-by-step instructions & troubleshooting tips to resolve errors effective
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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