where can I get pretrained unet, pixelnet, segnet deconvnet matlab codes for CT lung cancer segmentation?

This code fragment causes an error in the decoder and output layer. It provides a derror in the transponse layers of decoder and output layer, concatenation layers of the decoder, relu layerS of decoder and outputlayer and additional input missing and no input connection errors correspondigly.

% Define the UNet architecture
% Define input image size
inputSize = [120 150];
% Number of filters in the first layer
numFilters = 64;
% Number of segmentation classes
numClasses = 2;
% Define encoder layers
encoder_layers = [
    imageInputLayer(inputSize)
    
    convolution2dLayer(3, numFilters, 'Padding', 'same')
    reluLayer()
    convolution2dLayer(3, numFilters, 'Padding', 'same')
    reluLayer()
    maxPooling2dLayer(2, 'Stride', 2)
    
    convolution2dLayer(3, 2*numFilters, 'Padding', 'same')
    reluLayer()
    convolution2dLayer(3, 2*numFilters, 'Padding', 'same')
    reluLayer()
    maxPooling2dLayer(2, 'Stride', 2)
    
    convolution2dLayer(3, 4*numFilters, 'Padding', 'same')
    reluLayer()
    convolution2dLayer(3, 4*numFilters, 'Padding', 'same')
    reluLayer()
    maxPooling2dLayer(2, 'Stride', 2)
    convolution2dLayer(1, numClasses, 'Name', 'output')
    softmaxLayer('Name', 'softmax')
    pixelClassificationLayer('Name', 'pixel_class')
    ];
% Define decoder layers
decoder_layers = [
    transposedConv2dLayer(2, 2*numFilters, 'Stride', 2)
    reluLayer()
    concatenationLayer(3, 2, 'Name', 'concat_1'); % Connect to maxPooling2dLayer in encoder
    convolution2dLayer(3, 2*numFilters, 'Padding', 'same')
    reluLayer()
    convolution2dLayer(3, 2*numFilters, 'Padding', 'same')
    reluLayer()
    
    transposedConv2dLayer(2, numFilters, 'Stride', 2)
    reluLayer()
    concatenationLayer(3, 2, 'Name', 'concat_2'); % Connect to maxPooling2dLayer in encoder
    convolution2dLayer(3, numFilters, 'Padding', 'same')
    reluLayer()
    convolution2dLayer(3, numFilters, 'Padding', 'same')
    reluLayer()
    
    transposedConv2dLayer(2, numFilters/2, 'Stride', 2)
    reluLayer()
    concatenationLayer(3, 2, 'Name', 'concat_3'); % Connect to maxPooling2dLayer in encoder
    convolution2dLayer(3, numFilters/2, 'Padding', 'same')
    reluLayer()
    convolution2dLayer(3, numFilters/2, 'Padding', 'same')
    reluLayer()
    ];
outputLayer = [
    concatenationLayer(3, 2, 'Name', 'concat_3') % Connect to maxPooling2dLayer in encoder
    transposedConv2dLayer(2, numFilters/2, 'Stride', 2)
    reluLayer()
    convolution2dLayer(3, numFilters/2, 'Padding', 'same')
    reluLayer()
    convolution2dLayer(3, numFilters/2, 'Padding', 'same')
    reluLayer()
    
    concatenationLayer(3, 2, 'Name', 'concat_2') % Connect to maxPooling2dLayer in encoder
    transposedConv2dLayer(2, numFilters, 'Stride', 2)
    reluLayer()
    convolution2dLayer(3, numFilters, 'Padding', 'same')
    reluLayer()
    convolution2dLayer(3, numFilters, 'Padding', 'same')
    reluLayer()
    
    concatenationLayer(3, 2, 'Name', 'concat_1') % Connect to maxPooling2dLayer in encoder
    transposedConv2dLayer(2, 2*numFilters, 'Stride', 2)
    reluLayer()
    convolution2dLayer(3, 2*numFilters, 'Padding', 'same')
    reluLayer()
    convolution2dLayer(3, 2*numFilters, 'Padding', 'same')
    reluLayer()
    
    convolution2dLayer(1, numClasses, 'Name', 'output')
    softmaxLayer('Name', 'softmax')
    pixelClassificationLayer('Name', 'pixel_class')
    ];
output_network = layerGraph(outputLayer);
% Analyze the network to check output sizes
analyzeNetwork(output_network);

Error using analyzeNetwork
This functionality is not available on remote platforms.

[Walter Roberson]

You do not use encoder_layers or decoder_layers after you declare them.

It is correct that inside what you have labeled outputLayer that you do not have any output layer.

[Abel Belay]

this was a code fragment only specifying the layers, I did use them.

layers = [
    encoder_layers
    decoder_layers
%     convolution2dLayer(1, numClasses)
%     softmaxLayer()
 pixelClassificationLayer('Name', 'pixel_class')
];

pixellabel = [0 255];
categories = {'Lung', 'Tumor'};

imdsTrain = imageDatastore('D:\trainingimages');
pxdsTrain = pixelLabelDatastore('D:\masks',categories, pixellabel);

imdsValidation = imageDatastore('D:\validation');
pxdsValidation = pixelLabelDatastore('D:\validationlabel',categories, pixellabel);
trainData = pixelLabelImageDatastore(imdsTrain, pxdsTrain);
valData = pixelLabelImageDatastore(imdsValidation, pxdsValidation);

options = trainingOptions('adam', ...
    'MaxEpochs', 50, ...
    'MiniBatchSize', 16, ...
    'Shuffle', 'every-epoch', ...
    'InitialLearnRate', 1e-3, ...
    'Verbose', true, ...
    'Plots', 'training-progress', ...
    'ValidationData', {imdsValidation, pxdsValidation});

net = trainNetwork(trainData, layers, options);

but the trainNetwork command has caused "unconnected input) in several connections.