DATASetDir = fullfile('C:\Users\Akmal\Desktop\NEW 3D U NET 128X128');
IMAGEDir = fullfile(DATASetDir,'ImagesTr');
volReader = @(x) matRead(x);
volds = imageDatastore(IMAGEDir, ...
'FileExtensions','.mat','ReadFcn',volReader);
matFileDir = fullfile('C:\Users\Akmal\Desktop\NEW 3D U NET 128X128\LabelsTr');
classNames = ["background", "tumor"];
pxds = pixelLabelDatastore(matFileDir,classNames,pixelLabelID, ...
'FileExtensions','.mat','ReadFcn',@matRead);
ds = pixelLabelImageDatastore(volds,pxds);
patchSize = [128 128 64];
patchds = randomPatchExtractionDatastore(volds,pxds,patchSize, ...
'PatchesPerImage',patchPerImage);
patchds.MiniBatchSize = miniBatchSize;
dsTrain = transform(patchds,@augment3dPatch);
volLocVal = fullfile('C:\Users\Akmal\Desktop\NEW 3D U NET 128X128\imagesVal');
voldsVal = imageDatastore(volLocVal, ...
'FileExtensions','.mat','ReadFcn',volReader);
lblLocVal = fullfile('C:\Users\Akmal\Desktop\NEW 3D U NET 128X128\labelsVal');
pxdsVal = pixelLabelDatastore(lblLocVal,classNames,pixelLabelID, ...
'FileExtensions','.mat','ReadFcn',volReader);
dsVal = randomPatchExtractionDatastore(voldsVal,pxdsVal,patchSize, ...
'PatchesPerImage',patchPerImage);
dsVal.MiniBatchSize = miniBatchSize;
tbl = countEachLabel(pxds)
totalNumberOfPixels = sum(tbl.PixelCount);
frequency = tbl.PixelCount / totalNumberOfPixels;
inverseFrequency = 1./frequency
layerf = pixelClassificationLayer(...
'Classes',tbl.Name,'ClassWeights',inverseFrequency)
layerf=pixelClassificationLayer("Name","Segmentation-Layer")
image3dInputLayer([128 128 64 1],"Name","image3dinput")
convolution3dLayer([3 3 3],64,"Name","Encoder-Stage-1-Conv-1","Padding","same")
reluLayer("Name","Encoder-Stage-1-ReLU-1_1")
convolution3dLayer([3 3 3],64,"Name","Encoder-Stage-1-Conv-2","Padding","same")
reluLayer("Name","Encoder-Stage-1-ReLU-1_2")];
lgraph = addLayers(lgraph,tempLayers);
maxPooling3dLayer([5 5 5],"Name","Encoder-Stage-1-MaxPool_1","Padding","same")
convolution3dLayer([3 3 3],128,"Name","Encoder-Stage-1-MaxPool_2","Padding","same")
reluLayer("Name","Encoder-Stage-2-ReLU-1")
convolution3dLayer([3 3 3],128,"Name","Encoder-Stage-2-Conv-2","Padding","same")
reluLayer("Name","Encoder-Stage-2-ReLU-2")];
lgraph = addLayers(lgraph,tempLayers);
maxPooling3dLayer([5 5 5],"Name","Encoder-Stage-2-MaxPool","Padding","same")
convolution3dLayer([3 3 3],256,"Name","Encoder-Stage-3-Conv-1","Padding","same")
reluLayer("Name","Encoder-Stage-3-ReLU-1")
convolution3dLayer([3 3 3],256,"Name","conv3d","Padding","same")
reluLayer("Name","Encoder-Stage-3-ReLU-2")];
lgraph = addLayers(lgraph,tempLayers);
dropoutLayer(0.5,"Name","Encoder-Stage-3-DropOut")
maxPooling3dLayer([5 5 5],"Name","Encoder-Stage-3-MaxPool","Padding","same")
convolution3dLayer([3 3 3],512,"Name","Bridge-Conv-1","Padding","same")
reluLayer("Name","Bridge-ReLU-1")
convolution3dLayer([3 3 3],512,"Name","Bridge-Conv-2","Padding","same")
reluLayer("Name","Bridge-ReLU-2")
dropoutLayer(0.5,"Name","Bridge-DropOut")
transposedConv3dLayer([5 5 5],256,"Name","Decoder-Stage-1-UpConv","Cropping","same")
reluLayer("Name","Decoder-Stage-1-UpReLU")];
lgraph = addLayers(lgraph,tempLayers);
depthConcatenationLayer(2,"Name","Decoder-Stage-1-DepthConcatenation")
convolution3dLayer([3 3 3],256,"Name","Decoder-Stage-1-Conv-1","Padding","same")
reluLayer("Name","Decoder-Stage-1-ReLU-1")
convolution3dLayer([3 3 3],256,"Name","Decoder-Stage-1-Conv-2","Padding","same")
reluLayer("Name","Decoder-Stage-1-ReLU-2")
transposedConv3dLayer([5 5 5],128,"Name","Decoder-Stage-2-UpConv","Cropping","same")
reluLayer("Name","Decoder-Stage-2-UpReLU")];
lgraph = addLayers(lgraph,tempLayers);
depthConcatenationLayer(2,"Name","Decoder-Stage-2-DepthConcatenation")
convolution3dLayer([3 3 3],128,"Name","Decoder-Stage-2-Conv-1","Padding","same")
reluLayer("Name","Decoder-Stage-2-ReLU-1")
convolution3dLayer([3 3 3],128,"Name","Decoder-Stage-2-Conv-2","Padding","same")
reluLayer("Name","Decoder-Stage-2-ReLU-2")
transposedConv3dLayer([5 5 5],64,"Name","Decoder-Stage-3-UpConv","Cropping","same")
reluLayer("Name","Decoder-Stage-3-UpReLU")];
lgraph = addLayers(lgraph,tempLayers);
depthConcatenationLayer(2,"Name","Decoder-Stage-3-DepthConcatenation")
convolution3dLayer([3 3 3],64,"Name","Decoder-Stage-3-Conv-1","Padding","same")
reluLayer("Name","Decoder-Stage-3-ReLU-1")
convolution3dLayer([3 3 3],64,"Name","Decoder-Stage-3-Conv-2","Padding","same")
reluLayer("Name","Decoder-Stage-3-ReLU-2")
convolution3dLayer([1 1 1],3,"Name","Final-ConvolutionLayer","Padding","same")
softmaxLayer("Name","softmax")
pixelClassificationLayer("Name","pixel-class")];
lgraph = addLayers(lgraph,tempLayers);
lgraph = connectLayers(lgraph,"Encoder-Stage-1-ReLU-1_2","Encoder-Stage-1-MaxPool_1");
lgraph = connectLayers(lgraph,"Encoder-Stage-1-ReLU-1_2","Decoder-Stage-3-DepthConcatenation/in2");
lgraph = connectLayers(lgraph,"Encoder-Stage-2-ReLU-2","Encoder-Stage-2-MaxPool");
lgraph = connectLayers(lgraph,"Encoder-Stage-2-ReLU-2","Decoder-Stage-2-DepthConcatenation/in2");
lgraph = connectLayers(lgraph,"Encoder-Stage-3-ReLU-2","Encoder-Stage-3-DropOut");
lgraph = connectLayers(lgraph,"Encoder-Stage-3-ReLU-2","Decoder-Stage-1-DepthConcatenation/in1");
lgraph = connectLayers(lgraph,"Decoder-Stage-1-UpReLU","Decoder-Stage-1-DepthConcatenation/in2");
lgraph = connectLayers(lgraph,"Decoder-Stage-2-UpReLU","Decoder-Stage-2-DepthConcatenation/in1");
lgraph = connectLayers(lgraph,"Decoder-Stage-3-UpReLU","Decoder-Stage-3-DepthConcatenation/in1");
options = trainingOptions('adam', ...
'MaxEpochs',maxEpochs, ...
'InitialLearnRate',1e-3, ...
'LearnRateSchedule','piecewise', ...
'LearnRateDropPeriod',5, ...
'LearnRateDropFactor',0.97, ...
'ValidationData',dsVal, ...
'ValidationFrequency',50, ...
'Plots','training-progress', ...
'MiniBatchSize',miniBatchSize,...
'ExecutionEnvironment','cpu');
modelDateTime = datestr(now,'dd-mmm-yyyy-HH-MM-SS');
[net,info] = trainNetwork(dsTrain,lgraph,options);
save(['trained3DUNet-' modelDateTime '-Epoch-' num2str(maxEpochs) '.mat'],'net');
load('trained3DVNet-07-Jun-2022-13-45-30-Epoch-250.mat');